text:
\section{INTRODUCTION} \label{sec:intro}

Real-time detection and tracking of circular and elliptic shapes using an onboard vision system are essential for many real-world (mainly robotics) applications. For example, many aerial robot' landing zones consist of an elliptical shape~\cite{cargozone}, and autonomous cars need to detect the circular road signs~\cite{roadsign}.

Detecting ellipses in images has been a topic of interest for researchers for a long time \cite{tsuji1977detection, tsuji1978detection}. In general, it is possible to classify the available ellipse detection approaches into four classes.

The first class contains voting-based algorithms, including Hough Transform (HT) \cite{cite:hough} and the methods based on it. The HT algorithm uses a 5-dimensional parametric space for the ellipse detection task and is too slow for real-time applications. Other methods try to enhance HT by reducing the dimensionality in parametric space \cite{cite:houghdim1, cite:houghdim2, cite:zhang}, performing piecewise-linear approximation for the curved segments \cite{cite:houghapprox1} or randomizing the method \cite{cite:houghrand1, cite:houghrand2, cite:houghrand3}. Some of these modified HT-based methods are fast but generally less accurate and unsuitable for many robotics applications.

The second class contains optimization-based methods. Most popular methods convert the ellipse fitting problem into a least-squares optimization problem and solve the problem to find the best fit \cite{cite:fitls, fitzgibbon1995buyer}. These methods are generally not robust and tend to produce many false positives. However, their high processing speed is useful for quickly estimating the ellipse as the first step in other methods. Another group of optimization-based methods try to solve the non-linear optimization problem of fitting an ellipse using a genetic algorithm \cite{cite:genetic1, cite:genetic2}. These algorithms perform well in noisy images with multiple ellipses, but their processing time makes them impractical for real-time applications.

The third class consists of methods based on edge linking and curve grouping \cite{cite:mai}. These methods can detect ellipses from complex and noisy images but are generally computationally expensive and cannot be used in real-time applications.

The last class contains the methods that use an ad-hoc approach or combine the methods from the first three classes to achieve a faster and more accurate ellipse detection. A real-time method proposed by Nguyen et al. \cite{cite:nguyen} detects arc-segments from the image and groups them into elliptical arcs to estimate the ellipse parameters using a least-square optimization. A method proposed by Fornaciari et al.~\cite{fornaciari2014ellipsedetect} combines arc grouping with Hough Transform in a decomposed parameter space to estimate the ellipse parameters. In this way, it achieves a real-time performance comparable to slower, more robust algorithms.

Currently, in many robotics applications, the detection of the previously-known elliptic objects is done by general learning-based object detection methods~\cite{bhattacharya2021}. However, this approach requires collection and enhancement of large amount of data for each individual object and access to powerful computing systems for training. Recently, there has been an effort to implement a learning-based method specifically for ellipse detection~\cite{cnnellipse}. The method has been shown to work well for occlusions and partial views. However, it requires a powerful processor and is not yet suitable for real-time execution on onboard computers.

While many ellipse detection algorithms are proposed for computer vision tasks (\cite{WANG2021107741, Liu2020, 8972900}), the performance of these methods drops when used in real-world robotics tasks. Some of the challenges in these applications include:

\begin{itemize}[leftmargin=*]
\item The algorithm should work online (with a frequency greater than 10 Hz), generally on the robot's resource-limited onboard computer.

\item The elliptical object should be detected when it is far from or close to the robot.

\item The shape of the ellipse is transformed by a projective distortion, which occurs when the shape is seen from different views.

\item There is a wide range of possible illumination conditions (e.g., cloudy, sunny, morning, evening, indoor lighting).

\item Due to reflections (e.g., from sources like sun, bulbs), the pattern may not always be seen in all the frames, even when the camera is close.

\item In some frames, there may be shadows on the pattern (e.g., the shadow of the robot or trees around).

\item In some frames, the pattern may be seen only partially (due to occlusion or being outside of the camera view).

\item In some frames, the pattern may not be present.

\end{itemize}

Different approaches have been devised in the literature to detect and track an elliptic pattern in real applications to address the challenges above. For example, \cite{beul2018} uses the circular Hough-transform algorithm for the initial detection, which is a slow algorithm, and then uses other features in their pattern for the tracking. The authors of~\cite{jin2018} have developed a Convolutional Neural Network trained with over 5,000 images collected from the pattern moving at a maximum speed of $15$\,km/h at various heights to select (validate) the desired ellipse from all the ellipses detected using the  method in~\cite{jia2017}. The authors of~\cite{li2018} use the  method proposed in~\cite{fornaciari2014ellipsedetect} for ellipse detection only when their robot is far from the pattern and switch to other features in their pattern for the closer frames.

It is possible to use a predictor filter along with the detector to narrow down the region of interest (ROI) and improve the detection speed of ellipses across the frames. The authors of~\cite{ellipsekalman1} and \cite{ellipsekalman2} use Kalman filter to predict the direction of the ellipse movement in the frames, increase the detection speed and reduce the false positives. In many robotics applications, the combined simultaneous motions of the elliptic pattern, the robot, and the camera complicate the prediction provided by such predictors, resulting in the loss of tracking on many frames, triggering a detection on the entire frame and slowing the tracking speed.

This paper proposes a novel ellipse detection method that performs on resource-limited onboard computers in real-time. Our proposed ad-hoc method first extracts all contours from the input image and then uses a least-square method to fit an ellipse to each contour. It tests how well the estimated ellipse fits the contour and starts rejecting the contours using several criteria. The remaining contours are accepted as resulted ellipses, and if there are no contours left (e.g., due to the absence of ellipses in the frame), the algorithm returns an empty set of ellipses. The detection method is combined with a simple tracking algorithm that changes detection parameters as necessary and significantly increases the elliptical pattern detection's precision and performance. This simple tracker has been shown to be faster than using predictor filters due to the reduced tracking losses. The resulting detector and tracking method can deal with lighting variations and detect the pattern even when the view is partial. By comparing the results of our experiments on a collected dataset to the other methods tested on similar datasets we show that our method outperforms all the other real-time ellipse detection methods proposed so far.

Section~\ref{sec:approach} explains the developed method for detection and tracking of the elliptical targets; Section~\ref{sec:tests} shows an example real-time application that uses the proposed algorithm and compares the performance with some other available well-known methods. Finally, Section~\ref{sec:conclusion} discusses how the proposed method can be further improved in the future.

\section{ELLIPSE DETECTION AND TRACKING} \label{sec:approach}

The idea of the proposed ellipse detection algorithm is to fit ellipses to all the contours in a frame or the region of interest and then decide if the ellipse is a good fit. With real-time ellipse fitting algorithms and fast criteria for checking the fit, the result is a real-time detection algorithm that can detect ellipses as long as the elliptic pattern's contours are (at least partially) extracted during the contour extraction. With a suitable contour extraction method, the whole detection becomes robust to the lighting and environmental changes.

The pseudocode for the proposed ellipse detector is shown in Algorithm~\ref{alg:detection}.
\begin{algorithm}[!t]
\caption{Proposed approach for ellipse detection}
\label{alg:detection}
\begin{algorithmic}[1]
\LineComment {This function detects all the ellipses found using the input criteria (thresholds)}
\Function{DetectEllipses}{\textit{frame}, \textit{thresholds}}
    \LineComment {Initialize an empty set for the detected ellipses}
    \State $\textit{Detections} \gets \text{$\emptyset$}$
    \State
    \LineComment {Detect all edges in the frame}
    \State $\textit{edges} \gets \text{\textsc{DetectEdges}(\textit{frame})}$
    \State
    \LineComment {Extract contours from the detected edges}
    \State $\textit{contours} \gets \text{\textsc{ExtractContours}(\textit{edges})}$
    \State
    \ForAll {$\textit{c} \in \textit{contours}$}
        \LineComment {Reject if the contour is too small}
        \IIf {$|c.pixels| < minContourSize$} \Continue \EndIIf
        \State
        \LineComment {Fit an ellipse to each contour}
        \State $\textit{ellipse} \gets \text{\textsc{FitEllipse}(\textit{c})}$
        \State
        \LineComment {Reject ellipses with unreasonable dimensions}
        \IIf {$ellipse.largeAxis > mxAxSize$} \Continue \EndIIf
        \IIf {$ellipse.smallAxis < mnAxSize$} \Continue \EndIIf
        \State $\textit{axisRatio} \gets \textit{ellipse.largeAxis / ellipse.smallAxis}$
        \IIf {$axisRatio > maxAxisRatio$} \Continue \EndIIf
        \State
        \LineComment {Reject contours with small overlap with ellipse}
        \State $\textit{overlap} \gets \textit{$c.pixels  \  \cap \  ellipse.pixels$}$
        \IIf {$|\textit{overlap}| \textit{ / } |\textit{c.pixels}| \text{ is small}$} \Continue \EndIIf
        \State
        \LineComment {Reject ellipses with small overlap with the edges}
        \State $\textit{overlap} \gets \textit{$ellipse.pixels \  \cap \  edges.pixels$}$
        \IIf {$|\textit{overlap}| \textit{ / } |\textit{ellipse.pixels}| \text{ is small}$} \Continue \EndIIf
        \State
        \LineComment {Add the detected ellipse to the set of detections}
        \State $\textit{Detections} \text{.Insert(\textit{ellipse})}$
    \EndFor
    \State
    \LineComment {Return the detections after all contours are processed}
    \State \Return $\textit{Detections}$
\EndFunction
\end{algorithmic}
\end{algorithm}
The detection function receives a frame and a set of threshold values used in the function and returns a set of detected ellipses. A step-by-step explanation of the algorithm is as follows:

\begin{enumerate}[leftmargin=*]
\item The first step of the algorithm is to extract edges from the input frame and create the edge image. We used the Canny edge detector \cite{canny1986computational}, considering that the thresholds should be selected carefully to extract suitable edges in a large variety of conditions (e.g., illumination) while preventing the generation of too many edges. Usually, it is beneficial for smaller targets to produce more edges; this action will increase the processing time but reduces the probability of not having an edge for the elliptic target.

\item The resulted edges are utilized to extract contours using the algorithm proposed by Suzuki and Abe~\cite{suzuki1985topological}. This step helps make connections between relevant edges and enables the extraction of shapes in a frame. Ideally, each contour is a collection of connected points constructing a shape's border in the edge image.

\item Each contour is processed individually to determine if it is a part of an ellipse or not. For robotics tasks, an ellipse can have one of the following contour types:

\begin{figure}[t]
\centering
\includegraphics[width=\linewidth]{figures/circletypes.png}
\caption{Contour types of ellipses in different conditions: (a) A single contour containing a full ellipse. (b) A single contour containing a full ellipse with additional connected contours from the rest of the pattern. (c) Multiple contours, together constructing a full ellipse. (d) A single contour containing an ellipse that is partially occluded by another object. (e) A single contour containing a partially-seen ellipse. (f) A single contour containing a full ellipse with additional connected contour branches that is partially seen in the frame.}
\label{fig:circletypes}
\end{figure}

    \begin{itemize}[leftmargin=*]
    \item A single contour containing a full ellipse without any occlusions, broken segments or additional contours (Fig.~\ref{fig:circletypes}(a)).
    \item A single contour containing an ellipse with additional connected contours from the rest of the pattern (Fig.~\ref{fig:circletypes}(b)).
    \item Multiple contours, together constructing a full ellipse (Fig.~\ref{fig:circletypes}(c)).
    \item A single contour containing an ellipse that is partially occluded by other objects (Fig.~\ref{fig:circletypes}(d)).
    \item A single contour containing an ellipse that is partially seen in the frame (Fig.~\ref{fig:circletypes}(e)).
    \item A combination of the above contour types (Fig.~\ref{fig:circletypes}(f)).
    \end{itemize}

In order to correctly detect the above contour types, the following process is performed on each contour:

\begin{enumerate}[leftmargin=*]
  \item If a contour has a very small number of pixels, it is ignored since it is most probably just noise.

  \item An ellipse is fit to the contour using the least-square approximation method described by Fitzgibbon and Fisher \cite{fitzgibbon1995buyer}. The method fits an ellipse to any input contour; therefore, the contour should be further processed to determine the actual ellipses.

  \item The resulting ellipse will be ignored if any of the axes are too small or if the ellipse's eccentricity is high (close to 1). In high eccentricity, the resulting ellipse is similar to a line and is not really an ellipse.

  \item The current contour is intersected with the perimeter of the resulted ellipse, described by its center point, minor and major axes, and rotation angle. Then, the ratio of the number of pixels in the intersection to the number of all pixels in the contour is calculated:
  %
  \begin{equation}
    ContourOverlap = \frac{|Contour \  \cap \  Ellipse|}{|Contour|},
    \label{eq:01}
  \end{equation}

 where $| \cdot |$ is used for the number of pixels. A low result means that the contour and the resulted ellipse do not fit well, and a significant portion of the contour is not lying on the fitted ellipse. In this case, the contour is ignored and not further processed.

  \item Finally, the ellipse is intersected with the edges, and the ratio of the number of pixels in the intersection to the number of all the pixels in the ellipse is calculated:
  \begin{equation}
    EllipseOverlap = \frac{|Ellipse \  \cap \  Edges|}{|Ellipse|},
    \label{eq:02}
  \end{equation}

where $| \cdot |$ is used for the number of pixels. A low result means that a significant portion of the ellipse does not correspond to any contours in the image. The reason that the ellipse is intersected with the edge image instead of only its constructing contour is that due to noise or imperfect contour detection step, sometimes an ellipse is broken into two or more contours (e.g., the cases like Fig. \ref{fig:circletypes}(c)). In these cases, checking the ellipse against a single contour will give a low ratio and results in false negatives. To take care of the cases similar to Figure~\ref{fig:circletypes}(e), it is essential to count only the ellipse pixels that are actually lying in the image; otherwise, the result will be too low, and a partially viewed ellipse may get rejected. Additionally, to make the algorithm more robust to slightly imperfect ellipses, increasing the edges' thickness before intersecting them with the ellipse is beneficial.

  \item If an ellipse is not rejected in the previous steps, it represents a real ellipse in the image and is added to the set of detected ellipses.
\end{enumerate}

\item Due to the target ellipse's thickness, there is a chance of detecting two or more concentric ellipses. Therefore, the returned set of detected ellipses in the frame is further processed to find the concentric ellipses. All the non-concentric sets of ellipses are ignored when this happens.

\end{enumerate}

The proposed algorithm can also detect the ellipses with partial occlusion or the ellipses exceeding the image boundaries. The rejection criteria can be chosen in a way to accept ellipses in such cases.

Choosing higher rejection criteria for ellipse detection generally helps to eliminate potential false positives. Therefore, it is beneficial to have higher rejection thresholds when there is no prior information about the pattern location and size in the image. However, after the first detection of the elliptical pattern, it is possible to change the initial parameters and conditions for the detection to enhance the performance and increase the detection rate.
Decreasing the detection threshold values reduces the probability of losing the target ellipse in the following frames due to illumination variation, noise, occlusion, or other changes in the conditions. Additionally, if the approximate movement of the target is known, setting the region of interest (ROI) to the area where the pattern is expected to be seen will decrease both the processing time and the probability of falsely detecting other similar shapes that were rejected in the initial frame due to the higher thresholds.
Furthermore, whenever the detected target is large enough to be detected in the input frame with a smaller scale, the frame can be scaled down to increase the processing speed. Performing ellipse detection on the smaller frame takes less CPU time and is much faster.

We propose these steps that can be combined with the ellipse detection algorithm to track the detected elliptic pattern in the next frames more efficiently:

\begin{enumerate}[leftmargin=*]
\item Significantly decrease the $ContourOverlap$ and $EllipseOverlap$ threshold values. This threshold change increases the detection rate and helps the algorithm to keep tracking the target.
\item Determine the region of interest, which includes the current detected target and expands in all directions based on the distance from the target, the robot's relative speed and the pattern, and other available information. For example, if the distance is far and the relative linear and angular speeds are low, the target is expected to be found close to the current detection coordinates in the next frame.
\item Decreasing the scale of the frame by order of two (up until a set threshold) every time that the detected target is larger than a specified size and scaling the frame up again by order of two (up to the actual frame scale) every time the detected target is smaller than a chosen threshold. To make the approach more robust, ellipse detection with initial higher parameters is performed once again on a higher scale if no candidate targets are found on a lower scale. The scale change is performed to reduce the execution time, as the algorithm needs to process only a quarter number of pixels every time it scales the frame down.
\end{enumerate}

\begin{algorithm}
\caption{Proposed approach for elliptic target tracking}
\label{alg:tracking}
\begin{algorithmic}[1]
\LineComment {This function reads the frames from a video stream and performs tracking of a target}
\Function{TrackTarget}{\textit{videoStream}}

    \LineComment {Set frame scale, ROI and tracking status}
    \State \textit{isTracking} $\gets$ \textbf{false}
    \State \textit{scale} $\gets$ 1
    \State \textit{roi} $\gets$ $\emptyset$
	\State
	\While {videoStream $\neq$ $\emptyset$}
        \State \textit{frame} $\gets$ \textsc{ReadFrame}(\textit{videoStream})

        \LineComment {Detect the ellipse in the frame or ROI}
        \If {\textit{isTracking} $=$ \textbf{false}}
        	\State \textit{offsetTarget} $\gets$ \textsc{DetectTarget}(\textit{frame}, \textit{scale})
        \Else
            \State \textit{offsetTarget} $\gets$ \textsc{DetectTarget}(\textit{roi}, \textit{scale})
        \EndIf

    	\State
        \If {\textit{offsetTarget} $=$ $\emptyset$ \textbf{and} \textit{scale} $>$ 1}
        	\LineComment {If target not found, try on higher frame scale}
        	\State \textit{scale} $\gets$ \textit{scale} $\div$ 2
            \If {\textit{isTracking} $=$ \textbf{false}}
            	\State \textit{offsetTarget} $\gets$ \textsc{DetectTarget}(\textit{frame}, \textit{scale})
            \Else
            	\State \textit{offsetTarget} $\gets$ \textsc{DetectTarget}(\textit{roi}, \textit{scale})
            \EndIf
        \Else
        	\LineComment {If found, set the proper scale for next frame}
            \State \textit{isTracking} $\gets$ \textbf{true}
            \If {\textit{offsetTarget.size} $>$ \textit{maxTargetSize}}
                \If {\textit{scale} $<$ \textit{maxScale}}
            	    \State \textit{scale} $\gets$ \textit{scale} $\times$ 2
            	\EndIf
            \ElsIf {\textit{offsetTarget.size} $<$ \textit{minTargetSize}}
                \If {\textit{scale} $>$ 1}
            	    \State \textit{scale} $\gets$ \textit{scale} $\div$ 2
            	\EndIf
            \EndIf
        \EndIf
        \State

    	\LineComment {Compensate the target offset resulted from detection in ROI. Do nothing if ROI is null}
        \State \textit{target} $\gets$ \textsc{CompensateOffset}(\textit{offsetTarget}, \textit{roi})
        \State

    	\LineComment {Set the tracking status and ROI for the next frame}
        \If {\textit{target} $=$ $\emptyset$}
        	\State \textit{isTracking} $\gets$ \textbf{false}
        	\State \textit{roi} $\gets$ $\emptyset$
        \Else
        	\State \textit{isTracking} $\gets$ \textbf{true}
            \LineComment {Expand the area around the detected target}
            \State \textit{roi} $\gets$ \textsc{CalculateROI}(\textit{target})
        \EndIf
        \State

    	\State \Return $\textit{target}$
	\EndWhile
\EndFunction
\end{algorithmic}
\end{algorithm}

\begin{algorithm}
\caption{Proposed approach for elliptic target detection in a given frame scale}
\label{alg:targetdetection}
\begin{algorithmic}[1]
\LineComment {This function detects the target in the input frame at the specified frame scale}
\Function{DetectTarget}{\textit{frame}, \textit{scale}, \textit{isTracking}}
	\State
    \LineComment {Resize the frame to make the processing faster}
    \State \textit{scaledFrame} $\gets$ \textsc{ScaleImage}(\textit{frame}, 1 / \textit{scale})
    \State

    \LineComment {Set detection parameters based on inputs}
    \State \textit{thresholds} $\gets$ \textsc{DetermineParams}(\textit{isTracking}, \textit{scale})
    \State

    \LineComment {Detect ellipses using the specified thresholds}
    \State \textit{detections} $\gets$ \textsc{DetectEllipses}(\textit{scaledFrame}, \textit{thresholds})
    \State

    \LineComment {Select the actual target from the detected ellipses}
    \State \textit{scaledTarget} $\gets$ \textsc{SelectTarget}(\textit{detections})
    \State

    \LineComment {Rescale the detected target to original scale}
    \State \textit{target} $\gets$ \textsc{ScaleSize}(\textit{scaledTarget})
    \State

    \LineComment {Return the detected target (or $\emptyset$ if not detected)}
    \State \Return $\textit{target}$
\EndFunction
\end{algorithmic}
\end{algorithm}

Algorithms~\ref{alg:tracking} and~\ref{alg:targetdetection} show the described method in more detail.

\section{EXPERIMENTS AND RESULTS} \label{sec:tests}

\subsection{Elliptic Target Dataset and Parameter Selection} \label{sec:dataset}

We created a dataset with sequences recorded using a UAV from a stationary and moving vehicle carrying an elliptical platform at various distances, angles, and illumination conditions. The dataset contains 1,511 frames (1,378 positive and 133 negative frames) and 456 frames of a thinner version of the same pattern. The size of the frames is $640 \times 360$, and the ground truth for the detections is provided. The dataset can be accessed from \url{http://theairlab.org/landing-on-vehicle}.

The thresholds selection of the proposed ellipse detection algorithm depends on the tolerance for false positives vs. false negatives in the application. However, in practice, the detection is not too sensitive to the parameters in most cases, and they can be selected from a broad range. For our tests on the AirLab Elliptic Target Detection Dataset, we empirically chose the values shown in Table~\ref{tbl:ellipse-detection-parameters}. The GUI tool provided with the code helps with the calibration process letting the user see the parameters' effects in real-time. The ellipse detection parameters are independent of the lighting conditions. Therefore after a one-time calibration, the algorithm should detect the pattern in a wide range of weather conditions (e.g., sunny, cloudy, snowy) as long as the light is enough for the camera to capture the pattern.

\begin{table}[!t]
\centering
\begin{threeparttable}
\caption{Ellipse Detection parameters chosen for the tests on the AirLab Elliptic Target Detection Dataset.}
\label{tbl:ellipse-detection-parameters}
\begin{tabular}{|l|c|l|}
\hline
\rowcolor[HTML]{EFEFEF}
Parameter   & Value & Justification \\ \hline
\textit{ContourOverlap} for detection & 0.95 & To prevent False Positives. \\ \hline
\textit{ContourOverlap} for tracking  & 0.7 & To enhance target tracking.  \\ \hline
\textit{EllipseOverlap} for detection & 0.95 & To prevent False Positives.  \\ \hline
\textit{EllipseOverlap} for tracking  & 0.3 & To enhance target tracking.  \\ \hline
\end{tabular}
\end{threeparttable}
\end{table}

In order to assess the sensitivity of the algorithm against the thresholds, Figure~\ref{fig:contour-parameter-selection} shows the performance of the algorithm for different values of \textit{ContourOverlap} with the value of \textit{EllipseOverlap} fixed to $0.7$. Additionally, Figure~\ref{fig:ellipse-parameter-selection} shows the performance of the algorithm for different values of \textit{EllipseOverlap} with the value of \textit{ContourOverlap} fixed to $0.5$.

\begin{figure}[!t]
\centering
    \begin{subfigure}[b]{0.238\textwidth}
        \includegraphics[width=\textwidth]{figures/cot_measures.png}
        \caption{~}
    \end{subfigure}
    \hfill
    \begin{subfigure}[b]{0.238\textwidth}
        \includegraphics[width=\textwidth]{figures/cot_tptn.png}
        \caption{~}
    \end{subfigure}

    \medskip

    \begin{subfigure}[b]{0.238\textwidth}
        \includegraphics[width=\textwidth]{figures/cot_fpfn.png}
        \caption{~}
    \end{subfigure}
    \hfill
    \begin{subfigure}[b]{0.238\textwidth}
        \includegraphics[width=\textwidth]{figures/cot_times.png}
        \caption{~}
    \end{subfigure}
\caption{Performance and execution times of our algorithm vs. contour overlap threshold (\textit{ContourOverlap} parameter). The value of \textit{EllipseOverlap} is fixed to $0.7$ for all the experiments. Wrong Positives are the wrong detections when the target was present in the frame.
(a) Accuracy, Recall, and F1 Score of the algorithm increase with the increase of \textit{ContourOverlap} up to a point. (b) Increasing \textit{ContourOverlap} increases the number of True Negatives while may result in fewer True Positives after some point. (c) Increasing \textit{ContourOverlap} results in a lower number of False Positives and Wrong Detections, while it may result in an increase of the number of False Negatives after some point. (d) The execution time decreases by increasing the \textit{ContourOverlap} parameter.}
\label{fig:contour-parameter-selection}
\end{figure}

\begin{figure}[!t]
\centering
    \begin{subfigure}[b]{0.238\textwidth}
        \includegraphics[width=\textwidth]{figures/eot_measures.png}
        \caption{~}
    \end{subfigure}
    \hfill
    \begin{subfigure}[b]{0.238\textwidth}
        \includegraphics[width=\textwidth]{figures/eot_tptn.png}
        \caption{~}
    \end{subfigure}

    \medskip

    \begin{subfigure}[b]{0.238\textwidth}
        \includegraphics[width=\textwidth]{figures/eot_fpfn.png}
        \caption{~}
    \end{subfigure}
    \hfill
    \begin{subfigure}[b]{0.238\textwidth}
        \includegraphics[width=\textwidth]{figures/eot_times.png}
        \caption{~}
    \end{subfigure}
\caption{Performance of our algorithm vs. ellipse overlap threshold (\textit{EllipseOverlap} parameter). The value of \textit{ContourOverlap} is fixed to $0.5$ for all the experiments.
(a) Accuracy, Recall, and F1 Score of the algorithm very slowly increase with the increase of \textit{EllipseOverlap} up to a breaking point, where they suddenly drop. (b) Increasing \textit{EllipseOverlap} increases the number of True Negatives while reducing the number of True Positives after a certain point. (c) Increasing \textit{EllipseOverlap} results in a lower number of False Positives, while it may result in an increase of the number of False Negatives after some point. (d) The execution time generally decreases by increasing the \textit{EllipseOverlap} value.}
\label{fig:ellipse-parameter-selection}
\end{figure}

Let us define $N_{TP}$, $N_{FP}$, $N_{TN}$, $N_{FN}$, $N_{All}$, and $N_{WD}$ as the number of True Positive detections, False Positives, True Negatives, False Negatives, the total number of frames and the number of frames with visible targets where the detection was wrong. The measures in the plots are defined as follows using the five outcome types above.

\begin{itemize}[leftmargin=*]
\item Accuracy measures the ratio of all the frames that the algorithm gives the correct result (either the target is detected correctly or no target is detected in a frame without a target). It is defined as $(N_{TP} + N_{TN}) / N_{All}$.
\item Precision measures what ratio of all the target detections is actually correct. It is defined as $N_{TP} / (N_{TP} + N_{FP} + N_{WD})$.
\item Recall or sensitivity measures the ratio of all the frames containing targets that are correctly detected. It is defined as $N_{TP} / (N_{TP} + N_{FN} + N_{WD})$.
\item F1 Score is the weighted average of precision and recall and takes both false positives and false negatives into account. F1 Score is defined as $2 \times (Recall \times Precision) / (Recall + Precision)$.
\end{itemize}

It can be seen that increasing the value of \textit{ContourOverlap} up to some point generally decreases the number of false results (both false positives and false negatives) and therefore increases the number of correct results and statistical measures. Although, after some point, the number of detections (true or false positives) starts dropping, and the performance slowly decreases. Additionally, the higher \textit{ContourOverlap} results in more candidate contours being eliminated before further processing, reducing the algorithm's execution time.

On the other hand, the value of \textit{EllipseOverlap} has a smaller effect on performance and execution time, slightly improving the performance up to a point. If it is set too high, the algorithm starts rejecting more ellipses, causing a drop in the number of positive results, significantly decreasing the algorithm's performance.

The choice of other parameters in the algorithm depends on the application. For example, when the pattern is not expected to be far, setting a higher number for $minContourSize$ will improve the speed by eliminating the contours that cannot belong to the elliptic pattern. On the other hand, setting this parameter too high can result in false negatives when the pattern is seen partially or consists of several partial contours. In practice, however, the method has shown to be rather insensitive to these parameters, and generally, there is no need to change the default values. In all our tests, we have used the following values: $mnAxSize = 5$, $mxAxSize = 700$, $maxAxisRatio = 5$, $minContourSize = 50$, $maxScale = 100$.

\subsection{Comparison With Other Methods} \label{sec:comparison}

To compare the performance of our algorithm with other methods, we ran four other methods on the dataset introduced in Section~\ref{sec:dataset}:
the MATLAB implementation of a Hough Transform-inspired approach proposed by Xie \& Ji~\cite{xie2002ellipsedetect} with a random sub-sampling inspired by the work in~\cite{basca2005htsampling},
and the C++ implementations of the methods proposed by Fornaciari et al.~\cite{fornaciari2014ellipsedetect}, Prasad et al.~\cite{prasad2012}, and Jia et al.~\cite{jia2017}.
Table~\ref{tbl:ellipse-methods-performance} shows the results and performance of the algorithms on our dataset.

\begin{table}[!t]
\centering
\begin{threeparttable}
\caption{Performance of the ellipse detector methods on the AirLab Elliptic Target Detection Dataset.}
\label{tbl:ellipse-methods-performance}
\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|}
\hline
\rowcolor[HTML]{EFEFEF}
Method            & Accuracy$^{\star}$ & Precision$^{\star}$ & Recall$^{\star}$ & F1 Score$^{\star}$ \\ \hline
Ours         & \textbf{96.56\%}                                               & \textbf{99.77\%}                                                & \textbf{96.44\%}                                                           & \textbf{0.981}    \\ \hline
\cite{xie2002ellipsedetect}  & 3.64\%                                                & 3.64\%                                                 & 3.99\%                                                            & 0.038    \\ \hline
\cite{fornaciari2014ellipsedetect}  & 88.75\%                                               & 99.67\%                                                & 87.66\%                                                           & 0.933    \\ \hline
\cite{prasad2012}  & 89.81\%                                               & 99.45\%                                                & 90.22\%                                                           & 0.965    \\ \hline
\cite{jia2017}  & 78.36\%                                               & 95.50\%                                                & 80.04\%                                                           & 0.871    \\ \hline
\end{tabular}
    \begin{tablenotes}
      \small
      \item $^{\star}$ As defined in Section~\ref{sec:dataset}.
    \end{tablenotes}
\end{threeparttable}
\end{table}

The results show that our implemented algorithm outperforms the other methods in all the criteria. The method by Xie \& Ji's~\cite{xie2002ellipsedetect} was unable to perform well on the real frames of our test environment due to the relatively low resolution of our frames and the small size of the target in the frames; the few cases it could detect the elliptic target were when the target was covering a large portion of the frame. The main problem with the method by Fornaciari~\cite{fornaciari2014ellipsedetect}  was that it was unable to detect the elliptic targets when more than 25\% of the target was outside of the frame (case (e) in Figure~\ref{fig:circletypes}). The method by Jia et al.~\cite{jia2017} is comparatively fast but has a high false positive rate and is unable to detect the ellipses when they are far away (small in the frame). Prasad's method~\cite{prasad2012} has comparable performance to Fornaciari's but is significantly slower. At the same time, our proposed algorithm was fast and still able to detect the target's elliptic pattern in partial views or when it was small. Additionally, we should note that all the false positive cases of our algorithm on the dataset, detected elliptical drawings on the ground, which would have been rejected if the UAV's altitude information was used.

Figure~\ref{fig:deckresults} shows results for the detection of the elliptical pattern in some sample frames from the dataset. The method by Fornaciari et al. is unable to detect the ellipses in Figure~\ref{fig:deckresults}(a) and Figure~\ref{fig:deckresults}(d).

\begin{figure}[!t]
\centering
    \begin{subfigure}[b]{0.23\textwidth}
        \includegraphics[width=\textwidth]{figures/decktrack1.png}
        \caption{~}
    \end{subfigure}
    \hfill
    \begin{subfigure}[b]{0.23\textwidth}
        \includegraphics[width=\textwidth]{figures/decktrack2.png}
        \caption{~}
    \end{subfigure}

    \medskip

    \begin{subfigure}[b]{0.23\textwidth}
        \includegraphics[width=\textwidth]{figures/decktrack3.png}
        \caption{~}
    \end{subfigure}
    \hfill
    \begin{subfigure}[b]{0.23\textwidth}
        \includegraphics[width=\textwidth]{figures/decktrack4.png}
        \caption{~}
    \end{subfigure}
\caption{Result of the proposed algorithm on sample frames. The red ellipse indicates the detected pattern on the deck of the moving vehicle.}
\label{fig:deckresults}
\end{figure}

\begin{table*}[t]
\centering
\begin{threeparttable}
\caption{Execution times of ellipse detector methods on the AirLab Elliptic Target Detection Dataset.$^\star$}
\label{tbl:ellipse-methods-times}
\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|c|c|c|}
\hline
\multirow{2}{*}{\textbf{Method}} & \multicolumn{3}{c|}{\textbf{True Positive}} & \multicolumn{3}{c|}{\textbf{True Negative}} & \multicolumn{3}{c|}{\textbf{False Positive}} & \multicolumn{3}{c|}{\textbf{False Negative}} \\
& \textit{Avg.}       & \textit{Max.}      & \textit{Min.}      & \textit{Avg.}      & \textit{Max.}       & \textit{Min.}      & \textit{Avg.}       & \textit{Max.}       & \textit{Min.}      & \textit{Avg.}       & \textit{Max.}       & \textit{Min.}      \\ \hline
Our Method              & 32.2    & \textbf{64.1}   & \textbf{1.5}    & 48.9   & 68.1   & \textbf{6.6}   & 48.4    & 49.6    & 46.4   & 39.1    & 64.0   & \textbf{2.5}    \\ \hline
Fornaciari, et al \cite{fornaciari2014ellipsedetect
& 26.5    & 87.3   & 15.5   & 25.4   & 54.2    & 15.3   & -          & -          & -         & \textbf{23.8}    & \textbf{60.3}    & 14.7   \\ \hline
Prasad, et al \cite{prasad2012
& 36.2    & 70.0   & 25.5   & 34.4   & 64.9    & 20.0   & -          & -          & -         & 38.8    & 72.1   & 26.6   \\ \hline
Jia, et al \cite{jia2017
& \textbf{24.4}    & 67.3   & 13.1   & \textbf{21.2}   & \textbf{52.7}    & 11.6   & -          & -          & -         & \textbf{23.8}    & 63.3    & 12.8   \\ \hline
\end{tabular}
    \begin{tablenotes}
      \small
      \item $^\star$ All times are in milliseconds.

    \end{tablenotes}
\end{threeparttable}
\end{table*}

Table~\ref{tbl:ellipse-methods-times} compares the execution times of the ellipse detection methods on the same dataset (using Intel Core i5-4460 CPU @ 3.20 GHz). The implementation of Xie \& Ji's method is done in MATLAB, which gives much higher execution times than C++ implementations. Therefore we excluded it from Table~\ref{tbl:ellipse-methods-times}. Fornaciari et al. method has similar average execution times to our algorithm. However, their approach provides slightly more consistent execution times, which can be convenient for control systems using the detection output for robot control. On the other hand, our method's speed significantly increases (with frame processing time going down to just a few milliseconds) when the robot gets closer to the target pattern. This increase in speed especially helps the system to have a much higher detection rate when a higher processing speed is needed for the robot to approach the moving vehicle for landing.

\subsection{Example Application} \label{sec:application}

To test the proposed ellipse detection and tracking method's performance, it was used with a visual servoing method for an autonomous UAV landing on a circular pattern painted on top of moving platforms in indoor, outdoor, and simulated environments~\cite{keipour2021visualservoing}. Figure~\ref{fig:landing-tests} shows screenshots of the method in these different lighting conditions. Videos for these experiments and the project details can be accessed at \url{http://theairlab.org/landing-on-vehicle}.

\begin{figure}[!t]
\centering
    \begin{subfigure}[b]{0.23\textwidth}
        \includegraphics[width=\textwidth]{figures/resindoor2.png}
        \caption{~}
    \end{subfigure}
    \hfill
    \begin{subfigure}[b]{0.23\textwidth}
        \includegraphics[width=\textwidth]{figures/resfield4.png}
        \caption{~}
    \end{subfigure}

    \medskip

    \begin{subfigure}[b]{0.23\textwidth}
        \includegraphics[width=\textwidth, height=2.4cm]{figures/simulation1.jpg}
        \caption{~}
    \end{subfigure}
    \hfill
    \begin{subfigure}[b]{0.23\textwidth}
        \includegraphics[width=\textwidth, height=2.4cm]{figures/fpv.jpg}
        \caption{~}
    \end{subfigure}

\caption{Screenshots from video sequences showing our autonomous UAV landing on a pattern moving at up to $4.2$~m/s speed in various lighting and environmental conditions.}
\label{fig:landing-tests}
\end{figure}
\section{FUTURE WORK AND DISCUSSION} \label{sec:conclusion}

The proposed ellipse detection and tracking algorithm has shown its performance in an example application and has outperformed other standard methods in our tests. However, the proposed method has mainly been tested on a set of elliptical patterns used in our current and prior research. Utilizing the method for other applications may require further testing and improvements. The following suggestions can further enhance the performance and increase the method's usability in real-world robotics applications.

The underlying algorithms used in the ellipse detection steps are the most common methods already available in the OpenCV library. The choice has been made to allow fast implementation by the potential reader and convenience. If better performance is required, the whole method's execution speed and performance can be improved by replacing steps such as ellipse fitting with faster and better algorithms.

Finally, if the robot's camera is not perfectly rectified, it may lose track of the elliptic target at close distances where only a small portion of the pattern is visible at such a skewed angle that it causes the circle to be seen as non-elliptic in the camera. The problem exists for any ellipse detection algorithm but can be improved using a robust tracker (such as Kernelized or Discriminative correlation filters) instead of a detector to track the target ellipse when the robot's camera is too close to the elliptical pattern.

\section*{ACKNOWLEDGMENT}

Authors want to thank Near Earth Autonomy for their support in this project. The project was sponsored by Carnegie Mellon University Robotics Institute and Mohamed Bin Zayed International Robotics Challenge. During the realization of this work, G. Pereira was supported by UFMG and CNPq/Brazil.

\bibliographystyle{IEEEtran}

redpajama_set_name: RedPajamaArXiv


Every year, rare takes on new ecological restoration initiatives in order to enhance and rehabilitate the unique native habitats of this urban land trust. Join us in pulling out a combination of non-native plant species such as Daylily and Autumn Olive to allow native plants to grow. Native plants are the foundation of natural ecosystems and do a better job of providing food and shelter for native wildlife.


text:
Venezuela Rejects Inter-American Human Rights Commission Report
On Saturday, the Venezuelan government rejected the annual report of the Inter-American Commission on Human Rights (IACHR), which categorized Venezuela as one of four countries in the hemisphere where human rights are particularly threatened. President Hugo Chávez said Venezuela will consider withdrawing from the Organization of American States (OAS) and forming a separate regional organization with its allies.
By James Suggett – Venezuelanalysis.com
May 12th 2009 at 4.56pm
cidh.jpg
The headquarters of the Inter-American Commission on Human Rights (Telesur)
Human RightsOASMedia Coverage
Mérida, May 12th 2009 (Venezuelanalysis.com) - On Saturday, the Venezuelan government rejected the annual report of the Inter-American Commission on Human Rights (IACHR), which categorized Venezuela as one of four countries in the hemisphere where human rights are particularly threatened. President Hugo Chávez said Venezuela will consider withdrawing from the Organization of American States (OAS) and forming a separate regional organization with its allies.
The IACHR, which is an institution of the OAS, labeled Venezuela, Cuba, Haiti, and Colombia countries which "for diverse reasons confront situations that seriously and gravely affect the enjoyment of fundamental rights."
According to the report, Venezuela is "a hostile environment for political dissent." Human rights activists have received threats from unknown individuals, and the government has openly expressed its suspicion of human rights organizations that receive funding from international sources that may be hostile to the "21st Century Socialism" that Venezuela is constructing, the report says.
The report also alleges that the government violated freedom of religion by forcibly searching a Jewish community center in Caracas for weapon stashes. It highlights the increased rates of homicide and violent deaths in jails as signs that citizen security is weakly enforced.
Although the IACHR report focuses mainly on political and civil rights, it briefly "recognizes the importance" of the government's extensive social programs that have drastically improved Venezuelans' social and economic human rights, by cutting poverty in half, abolishing illiteracy, and making primary health care freely accessible to all Venezuelans.
In response to the report, the Foreign Relations Ministry released a statement in which it "categorically rejects" the "inexact, malicious, and false character of [the report's] affirmations," which lack "transparency and objectivity."
"The IACHR has abandoned its role as an international organism for the protection of human rights... and converted itself into a political instrument of the national and international sectors which, for ideological reasons, attack the progressive governments of the region," stated the Ministry.
The Ministry recounted how the IACHR recognized the interim government that was installed during a two-day coup d'etat in April 2002, during which sectors of the military and the elite business class kidnapped President Chávez and dissolved the constitution.
Also, from the time Venezuela ratified the American Convention on Human Rights in 1977 until the beginning of Chávez's presidency, which was a time period marked by many human rights crimes including the murder, disappearance, and torture of leftist political dissidents, the IACHR brought only six cases against Venezuela. During Chávez's presidency, in contrast, the IACHR has brought approximately 150 cases against Venezuela, according to the Ministry.
The Ministry demanded that the IACHR "apply the principles of universality, impartiality, objectivity, and non-selectivity in its examination of human rights issues, eliminate the application of a double standard and politicization," and "admit that it recognized the coup d'etat and rectify its biased position against our country."
Venezuela will continue to improve its human rights situation, "independently of the manipulations and lies of the IACHR," the Ministry's statement concluded.
During the televised inauguration of nearly two dozen new public health care centers on Saturday, President Chávez called the IACHR's report "immoral" and a sign of the "cynicism" of the OAS.
"Venezuela could leave the OAS and bring together the peoples of this continent to liberate ourselves from these old instruments, and to form an organization of free peoples of Latin America," said Chávez, who has already initiated several regional integration organizations based on cooperation and mutual benefit, as an alternative to U.S.-dominated free trade agreements.
"Why didn't [the report] condemn Bush?" Chávez asked, referring to U.S. President Barack Obama's predecessor. Chávez also said the report should have condemned the Obama administration's recent military assault on Afghanistan, which killed more than a hundred Afghan civilians.
The U.S. has never ratified the American Convention on Human Rights.
In response to the Venezuelan government's objections on Saturday, IACHR President Luz Patricia Mejía admitted that the private media, which are open adversaries of the Chávez government, provided the "majority of the information that the report possesses at present."
"We have openly questioned the use of the media as a principal source from which to formulate a general diagnosis of human rights in Venezuela, the use of media which have participated in an open and direct manner in political junctures that the country has lived and is living, and most of all, those which participated in an open and direct manner in the coup d'etat in 2002," said Mejía during an interview with the Caracas-based regional news station Telesur.
Mejía, a Venezuelan lawyer who previously worked with the Venezuelan human rights organization PROVEA, a frequent critic of the Chávez government, recommended that an "internal debate" be held during next month's summit of the OAS in Honduras.
Meanwhile, former Cuban President Fidel Castro expressed solidarity with Venezuela in the face of the OAS. "Today, Chávez is a formidable adversary of the capitalist system of production and of imperialism... No wonder the OAS is hypocritically trying to present him as an enemy of freedom of expression and democracy," Castro wrote in an editorial published in Cuban and South American news outlets. "Venezuela is not alone," wrote Castro.
This work is licensed under a Attribution Non-commercial Share Alike Creative Commons license
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The 2009 Annual Report by the Inter-American Human Rights Commission (IAHRC), which includes Venezuela among countries that do not respect human rights, is an...
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redpajama_set_name: RedPajamaCommonCrawl


Easter has been and gone already, which was (thanks to the upcoming royal wedding), the first of two back to back double bank holiday weekends. With work continuing to be horribly stressful I was determined to do something fun especially since Dave decided to stay at home rather than abandoning us for Maelstrom as is his usual habit.
Fun turned out to be a trip out to Coombe Abbey Country Park on Saturday for which Richard joined us and on Sunday we headed to Ryton Gardens.
Our last trip to Coombe Abbey took place just a couple of weeks before M was born, when walking any distance at all was painful on my poor swollen feet. Then, the park seemed impossibly huge as I tentatively picked my way along the paths... On Saturday however I'm pleased to say Coombe Abbey seemed much more manageable and we saw a lot more of the park.
April has been unreasonably warm and the full blossoms of a cherry tree provided welcome shelter from the sun.
After which we walked through some woodland with very big trees, finding an obstacle course and playground aimed at children much bigger than M. Naturally Dave had to put it to the test as did quite a few other dad's.
We had a picnic lunch and a very excited M was persuaded to have a sleep before we went for a brief wander beside the lake, eventually admitting defeat because the sun was too fierce.
Ryton Gardens lived up to expectations on Sunday and having visited there a few weeks ago it was fascinating to see how the place has changed in such a short time. With no particular agenda or schedule to keep to, we allowed M out of the sling so she could wander around and seeing her cruising around garden benches really brought home to me how fast she's growing. M was again very excited to be out and about; we had to hastily intervene several times to stop her eating sticks or leaves but she loved it.
I also discovered that eating a picnic lunch with an awake baby on my lap meant I was obliged to share.
All in all, both days out were good, relaxing and fun. Just what the doctor ordered and I mostly avoided thinking about work. With a double bank holiday I reverted to just being M's mummy for a couple of days, which was great.


text:
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Aaron is a white man who loves black women, a preference which was triggered by one woman; Tiffany Hyatt! While he never got with her, she set him on a path that would change his love life forever.
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"Bye, Mister Jacob," Billy Hanson called.
Aaron Jacob smiled as he waved to the ten year old running to his mom's blue minivan.
"Bye, Jimmy. See you tomorrow," Aaron returned.
Aaron walked briskly to his car that was parked in the teacher's lot. He unlocked and opened the door, tossed his briefcase into the back seat and got into his white Toyota Camry. He cautiously backed out of the parking space, careful to look for any wandering kids, then took off. He breathed deeply as he loosened his necktie and unbuttoned his collar. Today was a half day due to some quirky holiday and he was able to leave early.
Aaron wanted to take advantage of the extra time off and get to the gym early for a change. He would work out and then indulge in one of his favorite pastimes; walking on the beach. He was blessed that he lived in Jacksonville Florida and could go to the shore as much as he wanted to. He loved it there. It was peaceful and tranquil and he could think.
There were times when it was noisy with tourists and locals, but he knew where to find the quieter spots. He arrived quickly to the gym as there was no traffic. He grabbed his gym bag out of the trunk and went into the facility. There were only a few people in the gym and it was fairly quiet. Aaron wished he could come at this time every day.
He went into the locker room to change into his exercise clothes, then headed to the bench press. He looked at himself in the mirror before leaving and thought he looked pretty good. Women called him a fine hunk and men envied his physique. They thought his great body was a gift that God had generously bestowed upon him.
How wrong they were.
It took a lot of hard work to maintain his toned, muscular form. He liked cheesecake and fried chicken just like everyone else, but he had to refrain from eating too much of it or else he'd look like porky pig! Practicing good eating habits was essential if he wanted to maintain a healthy lifestyle.
He worked the bench press for about ten minutes then moved to the free weights. He selected a fifty pound weight, then sat on the bench. He'd started doing forearm rolls when he thought he saw someone whom he recognized. He looked closely to determine their identity. It could have been any African American woman, but there was something about this lady. Maybe it was the way she swayed her hips.
There was sassiness in her movements. It made him think of only one person. She finally turned so that he could see her full on. It was her; Tiffany Hyatt whom he'd had a crush on in high school. He'd never forgotten her sexy walk.
He'd been two years behind her and she hadn't given him a second glance. He had always wanted to talk to her, but had been too shy. He hadn't been sure if she was into Caucasian boys as the boys she had dated had always been African American.
Tiffany had been gorgeous, smart, outgoing and a popular girl who was always surrounded by friends. He remembered watching her at the football games. She'd been a cheerleader and her face had been wreathed in smiles as she routed and cheered for the team. He'd thought her beautiful as she jumped, kicked and pumped her pom-poms. He had imagined going to the movies with her and kissing her goodnight at her front door.
He had only been a freshman and her a junior when he fell head over heels in love with her. He realized that she would never have considered going out with him even if he had worked up the nerve to ask her. So he had loved her from afar until she graduated. His heart had been crushed when she left school, but time had eventually healed the hurt and he had moved on.
It had been twelve years since he'd felt that first twinge of puppy love. He was now a highly accredited fifth grade schoolteacher and he loved his job. He worked at a great elementary school and the staff and students were terrific. He wondered how the years had treated Tiffany. He saw her get on the stair stepper, but she was having trouble setting the controls. This was the perfect opportunity to finally approach her. He left the leg press and crossed the room.
"Need some help," he asked with a smile.
She looked at him gratefully.
"Yes. This machine is getting the better of me," she smiled wanly.
"These machines can be intimidating until you get used to them," he said. "Are you new to the gym?"
"Yes. I could stand to lose some weight and get into better shape," she said grimly. "I recently looked at my high school year book and was shocked when I realized I had gained about twenty-five pounds."
Aaron looked at her critically. She had gained a few pounds since high school, but not that much. It wouldn't take her long to reach her goal weight once she started working out. Funny how he hadn't noticed until she mentioned it. All he had seen was that she was as beautiful now as she had been in high school; even more so.
"You look good," he said kindly.
"You're sweet, but untruthful. I see myself in the mirror every day and I need to get into shape," she chuckled.
He grinned as he set the gauge on the machine.
"I beg to differ," he chuckled. "And I'm always honest. Here you go. Just press this button to set the pace."
"Thanks. How could something so simple escape me?" she smiled.
"You'll be alright from here on out. You should get a trainer. He would whip you into shape in no time," he said.
"Hmm, I don't think so. I'm not sure if coming to a gym will be my thing," Tiffany said.
Aaron frowned. Now that he had encountered her again, he didn't want to lose track of her.
"I would be happy to help you with your work out. I would hate to see you stop before getting started," he offered.
"Oh, I couldn't let you do that. I'm sure you have your own routine and helping me would just mess it up," she declined.
"Not at all. It's not a problem. I would only work with you until you got the hang of things," he said. "Besides, sometimes it's nice to have a workout buddy. You can keep each other motivated."
Tiffany looked at him as she moved up and down on the stepper. He seemed like a good guy and it was a nice offer. He appeared genuine and she could use all of the extra help she could get. Exercising wasn't her thing and she needed to be pushed.
"Okay. I accept your offer. Thank you so much," she said gratefully. "But you have to tell me when you get tired of working with me. I won't be offended."
"Deal," he said. "And my name is Aaron. Aaron Jacob."
She extended her hand.
"And mine is Tiffany Hyatt," she returned.
"I know," he said as he shook her offered hand.
"You know? How?" she questioned in obvious bafflement.
He mounted the stationary bike beside her.
"I remember you from high school. We attended Rolling Hills High together. I was two years behind you," he explained.
Tiffany looked at him intently. It was obvious she was trying to place him, then her face lit up in recognition.
"I remember you. You worked on the school newspaper," she recalled.
"Good memory. I wouldn't have thought you'd remember me," he said as he began cycling.
"Why wouldn't I? You were a great kid and I actually liked a lot of your articles," she said.
"Glad to hear it," he said. "Come on. Let's pick up the pace."
She increased her pace and he cycled faster. So she had noticed him. He didn't think she had. That still didn't mean she would have gone out with him, but that was years ago and they were just kids. He had changed a lot since then. The baby fat was gone; replaced by lean muscle. He was fit, attractive and confident, and he had every intention of pursuing Tiffany Hyatt. She just didn't know it yet.
Tiffany arrived at her bungalow cottage totally exhausted. She limped down the hall to her bedroom, tossed her purse onto the chaise and collapsed face down onto the bed; she could hear every bone in her body screaming in agony. She cursed her idea of going to the gym. It was the worst idea she had ever had. She must have been out of her mind to think that her body could take this kind of punishment.
Aaron had been patient and kind when she faulted in her attempt to do some of the exercises. He'd been supportive and encouraging; telling her that it would get easier. He lied. It wouldn't get easier. It would only get tougher, and she didn't know if she had the resolve to see this thing through. She knew that she should soak in a bath of Epsom salt. It would relax and soothe her aching muscles, but she didn't know if she could muster the energy to pull herself off the bed and prepare the bath.
She was so tired.
But if she didn't there would be hell to pay in the morning. She rolled over, sat up and then slowly walked into the bathroom. She took Epsom Salt out of the cupboard, turned on the tub water and laced it liberally with the salt. She sprinkled in bubble bath and then took off her clothes. She cringed in pain as she lifted one leg and then the other into the tub.
She sat in the tub and turned off the water, then lay back in the spa tub. The jetted water felt good against her sore limbs and after a while she could feel the salt taking the desired effect. She closed her eyes and thought about Aaron. Meeting him again after all of these years had been an unexpected turn of events. Had he not said who he was she would have never recognized him.
He certainly had changed since high school. She had not paid him much attention back in those days for an obvious reason. A girl would never look at a boy who was two years behind them, it just wasn't done. But he seemed to have grown up to be a very nice man, and despite feeling as though she'd been stretched on the rack, she would continue her sessions with him. She needed his help and was grateful that he had offered it. She just had to remember that all of this pain was worth the gain. Aaron had said that her muscles would get used to the tension she was applying and soon she would no longer get sore. She hoped that day would come sooner rather than later. She thought about the busy day ahead of her at the jewelry store. Tomorrow was not the day to be sore. She had far too much designing to do and too many customers to talk to. She realized that the soreness was beginning to leave her muscles. Thank I Am for Epsom Salt.
The next day turned out to be as expected for Tiffany. It had been non-stop from the moment she arrived at the store. Her morning had been busy making some very difficult cuts of diamonds and emerald nuggets. The cuts had been successful and now she was intent on shaving the stones. The entire process of cleaving, bruting, and polishing the pieces was tedious and painstaking. But she was extremely good at it which was how she was able to snag the job at such a young age.
At twenty-eight she held the position of senior diamond cutter at the prestigious Éclair Jewelers. Éclair was world renown for its exquisite jewels. It boasted having designed jewelry for the most celebrated clientele in all of the world. Many of those designs were hers, and she was very proud of them. Because of her extraordinary skill she'd been able to travel the world while taking part in intellectual gem studies and conferences. She had been blessed and she was very grateful. Tiffany was polishing a diamond when Marisa entered the backroom.
"Hey girl. Take a break. Have you had lunch?" Marisa asked as she sat on the stool in front of Tiffany's worktable.
Tiffany looked up at Marisa Faberge and smiled. Who needed a clock when she had Marisa? Her roommate came in every day promptly at twelve-thirty and asked if Tiffany had eaten lunch; knowing she would receive a negative answer.
"Not yet," Tiffany responded.
"We should go to that new Italian place that opened last week. The reviews are good," Marisa suggested. Marisa kicked off her heels and crossed her leg over her knee to rub her foot. "My feet are killing me."
"Vanity can be painful," Tiffany grinned. "You insist on wearing those stilts when you know you'll be on your feet all day."
"Showing expensive jewelry looks better when presented in heels," Marisa defended.
Tiffany laughed.
"It's got nothing to do with the look of the diamond. Most of our customers are men and you want to show off your legs," Tiffany joked.
"You never know when Mister Right will walk in. I have to be ready," Marisa smiled unrepentantly.
"Then don't complain about sore feet," Tiffany quipped.
Tiffany observed Marisa's beautiful features. Her smooth coffee skin was supple and soft and her cherry brown eyes attracted men like collard greens drew flies. With her slender 5'5'' frame and long dark hair she became the center of attention the moment she entered a room. Now that Marisa had decided to settle down it was only a matter of time before Mr. Right tapped on her shoulder.
"Speaking of sore limbs, how did your gym experiment go yesterday?" Marisa asked.
"It went well but I was sore as all get out when I got home," Tiffany said. "My eagerness to get into shape is beginning to wan, but something interesting did happen."
Tiffany moved to put away the diamond she was working on. She fiddled around storing the gem and locking the safe.
"Well? Are you going to tell me?" Marisa asked excitedly.
Tiffany turned to face her and grinned.
"It's not earth shattering. I just met someone—a man," Tiffany said casually.
"What! Who? What? How? Answer all the questions, girl," Marisa exclaimed.
"Calm down, girl. It's not what you think," Tiffany replied.
Tiffany proceeded to tell Marisa about meeting Aaron and what had ensued.
"So, you're going to be training with him?" Marisa asked.
"Yes. He offered and I accepted. Lord knows I can use his expertise," Tiffany said.
"And that's all?" Marisa asked disappointedly.
"Yes. What else is there?" Tiffany asked in bafflement.
"So, this is just a training buddy thing. You're not the least bit attracted to him?" Marisa asked.
"No. There are no romantic sparks going off between us. Sorry to disappoint you," Tiffany grinned.
"Darn! I had selected your wedding colors," Marisa grinned.
"Girl, please! Come on. Let's check out that Italian restaurant," Tiffany said.
"Okay," Marisa replied as she put her shoe back on. "Let's go."
They left the jewelry store and chatted as they walked to the restaurant.
Aaron had been working out for twenty minutes and there was still no sign of Tiffany. He'd been watching the door so much that he had lost his focus on his own workout.
"Is she not coming?" he wondered sadly.
He hadn't taken her as a flake. The description just didn't ring true where she was concerned. But there was denying the fact that she wasn't there. She could be running late but there was always time to call and say she was delayed. Aaron tried to cast off the disappointment as he worked with the free weight, but the disillusionment remained with him like a heavy mantel on his shoulders. He glanced at the door again and saw her rushing in. He felt a sudden relief. Her not showing affected him more than he wanted to admit. He knew it was early on but he was invested in her progress and he wanted her to be as well. He was also invested in her personally—but that was another story. She waved at him and pointed to the locker room; indicating that she was going in to change. He waved back and smiled. When she joined him she was all apologetic.
"I'm so sorry I am late. I'm really not a late person but I was tied up at work and I couldn't very well blow off my boss," she smiled wanly.
"It's not a problem. I understand. How do you feel today?" he asked in concern.
Tiffany could see that he was sincerely interested and that warmed her heart.
"Not nearly as bad as I feared. I bathed in Epsom Salt and it really relieved the soreness in my muscles," Tiffany explained.
Aaron had a visual of a naked Tiffany in the bath tub. He had to work hard not to allow his package to show the affects her words had on him. He cleared his throat.
"I'm glad you're feeling better. Ready to start?" he asked.
"Mm hmm. What will we do first?" she asked.
He grinned.
"You mean: what will you do first?" he teased. "I thought we could focus your attention on toning your arms with free weights."
Click to read the rest of this BWWM / BBW novel.
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Journals / Property Journal / What does Large ruling mean for surveyors?
PROPERTY JOURNAL
What does Large ruling mean for surveyors?
With a surveyor found liable for failing to advise in a report on the risks presented by a property, RICS held an expert round-table to explore the case's implications for professionals
Residential valuation
RICS' response
RICS has been engaging with the profession and experts extensively in recent months to gain insight on the implications of Large. With the support of legal experts, we reviewed the new Home Survey Standard in the context of this case, and it was found to be fit for purpose and robust. The round-table demonstrates that members' views mirror this finding.
The new RICS Home Survey Reports section B, condition rating R offers members the space to highlight documents they suggest clients request before they commit to the property transaction. Where they are in doubt, we advise them to use professional judgement and request a Professional Consultants Certificate.
The round-table highlighted the need for further support. RICS will seek views from the profession and engage with members to develop materials and pre-inspection checklists to be included in the Surveyor Toolkit.
The implications of this case will also be considered in future training relevant to RICS home surveys, and in advice such as the forthcoming Risk, liability and insurance guidance note. RICS will continue to engage with members and the wider profession to increase awareness of the issues raised in Large and enhance consumer education in the homebuying and selling process.
Finally, RICS members are encouraged to continue sharing their experiences and views about the case and its implications on the RICS Yammer Insight Community.
Following the conclusion of Large v Hart & Anor [2021] EWCA Civ 24 in the Court of Appeal – in which a surveyor, Mr Large, was found liable for the costs of repairing a property the Harts had purchased on the strength of a HomeBuyer Report he had provided – RICS organised a round-table discussion on the case and its potential impact for surveyors.
Has the decision altered how surveyors work? Will it lead to more expensive insurance cover or more frequent claims in the future? And what advice can RICS offer members to avoid a similar risk during their own works?
The legal context
The discussion opened with an overview of the case from Alexandra (Alex) Anderson of legal firm Reynolds Porter Chamberlain. Large had been asked to produce a HomeBuyer Report for an extensive renovation of a hilltop property in Devon, in which he highlighted some minor issues related to guttering and pipes. He valued the property at £1.2m, and the Harts purchased it at that price.
There had been some discussion as to whether it was necessary to obtain a professional consultants certificate (PCC) from the architect that had been responsible for inspecting or supervising the works, and when asked by the Harts if they should obtain one Large did not answer 'Yes': this is the point on which much of this case turns.
Following the purchase, the property suffered serious water ingress and damp problems. The Harts brought a negligence claim against Large as well as the solicitor and the supervising architect. Both the architect and solicitor settled, so the claim went to trial against Large.
There were three allegations.
Large should have advised that the HomeBuyer Report was not the right level of survey for the property; this charge failed.
There were features that could not have been surveyed or verified, including a damp-proof coursing that was not there at the time of the survey. The original judge concluded this should have put Large on a 'trail of suspicion', and he should have warned that further investigation was required.
Large should have advised his clients to request the PCC before proceeding with the purchase. This was the only way they could have protected themselves against the risk that the renovation works had not been carried out properly, as well as against any latent defects that Large couldn't possibly ascertain in preparing a HomeBuyer Report.
In addition, the Harts brought evidence that, had they been told to request the PCC, they would have found that the architect was not prepared to give the certificate.
Effectively, the judge treated this as what is known as a no-transaction case, which brings into play the principles established in South Australia Asset Management Corporation v York Montague Ltd [1996] UKHL 10. These principles had been applied in BPE v Hughes-Holland [2017] UKSC 21.
No-transaction
In no-transaction cases the distinction is drawn between whether advice or information was given. If you provide information, then you will not be responsible for all of the losses that may occur if the transaction goes ahead. However, the original judge concluded that Hart & Anor v Large & Ors [2020] EWHC 985 (TCC) was an advice case because, by giving the fundamental advice to obtain the PCC, Large would either have stopped the transaction going ahead – as there was no certificate – or he would have transferred the risk of the latent defects on to the architect who provided the certificate.
Because this was considered an advice case, Large was deemed responsible for all financial implications of the transaction, and therefore had to meet the cost of remedying all defects to the property.
The case was sent to the Court of Appeal, which upheld the original decision. Judge Coulson concluded that in order to compensate the Harts properly, it was necessary to take into account all the defects with the property rather than just looking at those that the court concluded Large had missed.
The case raises questions about what surveyors need to be doing to protect their clients and themselves. The key message seems to be whether there is a way to offload the risk of latent defects from the purchaser to a third party such as an architect or solicitor, so that the former is not responsible for defects that could not have been identified in a survey but that have a significant effect on the property value.
Following this summary of the case, round-table chair Paul Bagust of RICS opened the call to questions and discussion from attendees.
The certificate
Jeff Smith asked how far we should be taking the PCC into consideration. If there is a three-bedroom, semi-detached house to which someone wants to add a two-storey extension at the side, this could increase the floor area of that property by about 50%. Should surveyors be calling for PCCs in these circumstances? Previously, we've just accepted building completion certificates.
Alex advised that in this scenario the PCC should be requested. If refurbishment works have been undertaken – particularly where there has been an increase in the property by the proportion seen in this case – then a surveyor should advise the client seek a PCC. This at a minimum reduces your risk, because it means that the claimant cannot ask why they weren't told me that this was necessary. In Hart, the judge after all said that a PCC should have been requested, and the case is already being cited in claims.
As long as a surveyor has said to the client that there have been refurbishment works, it is the client's responsibility to check whether there is a PCC. Where there is a certificate the surveyor should obtain a copy. Once this is done then you as a professional have discharged your duty according to what was said in Large: that is, the client has been put on notice that seeking a certificate may be a way for them to protect themselves, and you have suggested they should get that protection.
Documentation matrix proposed
Malcolm Hollis reminded us that it is important to remember that surveyors should not be exploring or opening up buildings to check for every possible defect or missing damp-proof course, and the surveyors' report should clarify its own limitations. Phil Parnham listed the key features of the property in Large – such as the clifftop location, tanked habitable rooms, and a balcony providing level access to bifold doors. Taken as a whole, these indicate that it is a very complex property.
When asked to provide a HomeBuyer Report, the question you should ask yourself is whether this offers an appropriate level of service. In this case it would not be – the property required a level 3 survey, which offers an extensive inspection of property condition, structure and fabric, and therefore Phil suggested that RICS' Home Survey Standard would have been more suitable.
In addition to requesting a PCC, a surveyor should consider individual elements, and explain to clients they need to check with legal advisers that the relevant Building Regulations have been fulfilled and that planning and contract documentation has been completed before going through with a purchase. This comprehensive approach is already outlined in the HomeBuyer Report, and therefore this can still be considered fit for purpose.
What is the appropriate survey?
Steve Lees reiterated that if you work through the points in the Home Survey Standard, the property in Large clearly fell out of scope for a level 2 survey, which is the key message for surveyors from this case. Similarly, Laurence Weill noted that Large had stated that the property had been completely rebuilt, which meant the PCC became relevant, whereas a survey of an extension – where many features are hidden – may result in a different or more cautious approach.
When asked how the case was affecting firms, Angela Chamberlain noted that her company is now providing level 3 surveys as a default and looking at its report format to identify appropriate risk levels. Each element of the property is now given a red, amber or green rating, and the report also outlines what the client needs to take account of and what further investigations need to be made, as well as providing the usual commentary on findings and advice.
On the same question, Joe Arnold said that Arnold & Baldwin has gone through all its reporting to ensure that staff are asking the right questions of the properties surveyed, as well as double-checking whether level 2 or level 3 are required, and that its surveyors are raising the right queries with legal advisers. Both Joe and Gary Epps noted that the wrong outcome from this case would be a shift towards defensive reporting and a failure to give customers the right advice just so as to guard against potential liability.
Gary reiterated Phil's point that, from a surveyor's point of view, complexity is perhaps the most important thing to watch for, and the correct product should be chosen on that basis. He also noted that if you request a PCC and are only offered a retrospective one, most lenders won't accept it, so surveyors should be very wary about what is or isn't included in a certificate.
Day-to-day impact
Noting that the round-table consensus was that Large is a unique case, Paul asked whether individuals felt this was affecting their day-to-day business.
Laurence responded by noting that it is likely to make surveys more expensive, as there is a shift towards providing continuous advice and further questioning of third parties, which will add up to hours of extra work. There is a worry that clients will not accept this extra cost or see it as an attempt to upsell them for no reason. It is also important that the public are asked to keep copies of construction plans, because it is commonly the case that clients do not have their plans, even with recently built properties, which has further implications.
Chris Rispin noted that the courts were extremely pedantic about how they looked at Large, and surveyors need to reflect the level of detail required in their reports to mirror this. He also noted that you detail repairs in a level 2 survey, but Large raises the question of whether this counts as information or advice, and he wondered what the implications of that are.
Alex reiterated that the Court of Appeal has said there isn't a clear demarcation between information and advice and there can be ambiguous situations, so surveyors need to be extremely careful to ask whether any advice given is going to the root of the transaction – in which case, it is less likely to count as information.
Paul asked whether any insurers could give their professional reactions to the case. Chris Moseley stated that there is nothing positive about Large, and from an insurer's point of view it emphasises that surveyors' work is high-risk, with a high frequency of claims as well as high costs. Chris noted that most insurers don't want to go to court and that his company would likely have wanted to settle if the surveyor were not absolutely certain of their position.
Conclusion - points to highlight
Ana Bajri stated that one key outcome of Large will be the need to increase awareness among professionals and consumers that documentation should be stored safely. Consumers should also be clear what steps they need to take before requesting a survey.
Adding to this point, Ian Bullock stated that clients are not generally aware of what a PCC is, so raising awareness is key. Furthermore, the client needs to be made aware of what information they need from their lawyers and how to act on that.
Andrew Peters also reiterated what was a common theme of the round-table: although the PCC is now more important, it cannot be requested or may not exist in every circumstance. It's therefore important that the request is proportionate to the work undertaken, and surveyors should ensure that the advice given to the client when the certificate is not available is also proportionate.
Brian Ward is an editor of RICS Property Journal bward@rics.org
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Wizards Make Big Move, Flip Wall for Russ
By BradleyBeal.Net Staff| 2020-12-09T18:10:19-05:00	December 4th, 2020|News|0 Comments
Bradley Beal had been looking forward to the return of an All-Star to pair with in the Washington Wizards backcourt in 2020-21.
Instead he'll have a new All-Star by his side when the season begins later this month.
The Wizards announced on Thursday that they have agreed to trade long-time D.C. point guard John Wall to the Houston Rockets in exchange for Russell Westbrook. The move pairs two of the Top 7 scorers in the league last year with Brad and Westbrook in Washington. The move left Brad with mixed emotions as he says goodbye to a long-time teammate and friend while welcoming in one of the most talented players in the game. Speaking with the media the day after the trade, BB reflected before looking ahead.
"Definitely been a tough 24, 48, 72 hours here, just kind of letting it all seep in and just realizing that your brother is no longer here with you. It's a tough pill to swallow, you know…Our brotherhood continues. I've had conversations with John over the last few days, and they've been nothing put positivity and nothing but great conversations," Brad said. "But today was the first day of camp, so I kind of have to shift my focus to the Wizards and bringing Russ in and welcoming him and making him feel comfortable, getting this thing off and running."
Pairing alongside each other will certainly be an adjustment for both Brad and Westbrook. Since Wall went down with his Achilles injury, Brad has become much more accustomed to playing with the ball in his hands as the primary options, and he's flourished in that role. He'll have to adapt again with Westbrook on the ball a large portion of the time, but it's an adjustment he's ready and willing to make.
"I'm definitely curious to see the adjustment I make playing with Russ," Beal said. "I will say I know him off the court a little bit. I am a fan of his. He's a family man, great guy, great character guy. So I believe we'll mesh well and jell well. . . . I'm adaptable, very easy to play with, so I don't think it'll be a problem at all."
Washington coach Scott Brooks, who coached Westbrook early in his career in Oklahoma City, believes his two stars will push each other to be better and the Wizards will benefit.
"They're determined guys; they have a big-time drive in their work ethic, their professionalism, what they're about off the court. There's a lot of things — they remind me of each other," Brooks said. "So there's going to be some figuring out to do with myself and my staff. But they're about sacrificing. We know, if we want to get to the place we want to get to, we have to sacrifice. … It's going to take an entire 15-man roster to make some sacrifices, where we want to get to."
The Wizards begin preseason action on Sunday, December 13 in Brooklyn. Their regular season begins on December 23 in Philadelphia against the 76ers.
Beal Reacts To Wednesday's Horrific Events in D.C.
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Lithuania will have to improve its reputation in Rail Baltica project
2016 m. december 21 d. 16:26
Rokas Masiulis
Lithuania will have to improve its reputation that has been harmed by the country's inability to find common ground with its partners in Rail Baltica and to prove that it is not holding back the European standard-gauge railway project, Rokas Masiulis, the new transport minister, said.
"The first thing to be fixed is Lithuania's reputation. Lithuania was accused -- perhaps in part rightly, but, I am sure, in part wrongly -- of acting as a brake on Rail Baltica. We did not always find agreement among ourselves: the ministry was at odds with the railway operator and the latter locked itself in a fortress, and we in Lithuania did not do our homework to look solid from outside. Other countries made use of this and Lithuania was blamed for the project's problems," he said in an interview with BNS.
Masiulis said that he would ask Lietuvos Geležinkeliai (Lithuanian Railways) and the Transport Ministry's staff to work to restore the country's reputation as a strong and reliable partner in the project.
Lithuanian Railways has foreign policy of its own
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"I will ask both Lietuvos Geležinkeliai and the ministry's staff to rebuild Lithuania's reputation so as to leave no chance for accusing us of anything and to be a leader for others to follow," he said.
Disagreements between the three Baltic countries started when Lithuania insisted on including a spur from Kaunas to Vilnius in the Rail Baltica project, which was eventually achieved after lengthy discussions.
Differences then emerged over the establishment of RB Baltic, a Baltic joint venture coordinating the project. Lithuania dropped its initial opposition to the idea of setting up such a joint venture, but it demanded that the VAT for the work done in Lithuania should stay in the country, rather than going to Latvia where RB Rail is based.
The countries reached an agreement on VAT and a public procurement model last September, a year later than expected. However, Stasys Dailydka, the then CEO of Lietuvos Geležinkeliai, did not want to sign the deal, saying that it was disadvantageous to the state railway company. He signed it after the European Commission threatened to suspend funding for Lithuanian for the project.
Lithuania also clashed with Baiba Rubesa, chairwoman of the management board at RB Rail. Dailydka said last May that RB Rail had become "a stumbling block" to the joint project and accused Rubesa of not working for the benefit of all three states.


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