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Paper text:
the scale invariant feature transform ( sift ) @xcite has been the topic of many computer vision projects due to its ability to detect feature points which are invariant to image rotation , translation , and scaling .
this is obviously evident from figure 1 which presents the journal and conference papers related to the sift algorithm which have been published in elsevier from 2009 to 2014 .
while several scientists have entered the development of sift based algorithm @xcite , @xcite , @xcite , @xcite , providing a service - based sift algorithm over the internet protocols has not yet been developed .
having a software as a service ( saas ) based @xcite , @xcite sift algorithm can be fulfilled the following objectives :        * to facilitate rapid application development ( rad ) and fast prototyping for computer vision students and scientists by a tiny service available on the internet . * to provide application - to - application interaction for a highly demanded computer vision algorithm . * to make the sift algorithm available through both human - oriented and application - oriented interfaces .    in this contribution
, we novel design and develop a saas based architecture to implement a platform independent and reusable software component for the sift algorithm .
the service makes a new way to produce and exchange of sift information over the standard internet protocols . based on its flexibility and availability
, it brings an opportunity for computer vision researchers who want to apply the sift algorithm on their own images without the need of any programming and installing any software applications .
an internet connection is all we need !    this work initiates a study of saas based architecture for an essential computer vision technique .
we expect the work can bridge the gap between computer vision applications and world wide web services , stimulating more interests from the computer vision community to the saas architecture and the internet of things ( iot ) areas .
our contribution could be considered as a preliminary step towards the * computer vision as a service*.    the rest of the paper is arranged as follows .
we first give a brief explanation of the system and service design in section 2 .
the experimental results along with an application of the sift algorithm and the impact of the service are shown in section 3 .
conclusion is presented in section 4 .
sift algorithm has basically four main stages , namely : 1 ) scale - space construction using difference - of - gaussian ( dog ) , 2 ) stable features localization , 3 ) gradient orientation computation and magnitude assignment , and 4 ) feature descriptors extraction @xcite .
the first stage is to construct a dog image pyramid to determine the potential feature points in an image . as it is shown in equation ( 1 ) , to create gaussian filtered image , we should convolve the input image @xmath0 with a gaussian kernel @xmath1 ( equation ( 2 ) ) , where @xmath2 is the scale of the gaussian kernel , and @xmath3 is 2d convolution operation .
various gaussian blurred images in different scales are produced and dogs are computed from neighbors in the scale space . then candidate feature points would be detected by discovering exterma in the dog images which are locally minima or maxima in scale and space .
@xmath4    @xmath5    the next step is to compute the image gradient magnitude and principal orientation to extract associate sift descriptor for the a detected feature point .
gradient magnitude as @xmath6 and principal orientation as @xmath7 are calculated using the following two equations :    @xmath8    @xmath9    finally , for each feature point , a set of orientation histograms will be created on @xmath10 pixel neighborhoods with eight bins each .
the size of the descriptor vector can be varied .
the high level service architecture of the siftservice.com is shown in figure 2
.        the service compromises two disparate machines : a service consumer ( client ) and the service provider ( application server ) @xcite .
main components of each section are presented within the subsystem blocks ( figure 2 ) .
wsdl stands for web service description language , and it is an xml - based language for describing sift web service and how to access it over the internet protocols .
discovery services permit the discovery of the sift service .
bpelws stands for business process execution language for web service , and it aims to support the major behaviors of both executable and abstract implementation of the service .
soap stands for simple object access protocol @xcite , @xcite .
soap is an xml - based internet protocol which supports exchanging structured information between computers and applications .
uddi which stands for universal description , discovery and integration is a specification for a distributed registry of the sift service @xcite , @xcite , @xcite , @xcite .
an snapshot of siftservice.com is shown in figure 3 .
in order to examine the general performance of the system , two experiments were carried out on real digital images .
section 3.1 shows the experimental setup . in section 3.2
, we compare the feature points detection accuracy of the system with the original executable sift software implemented by david lowe @xcite . in section 3.3 ,
we analyze and compare matching accuracy between sift keypoints in two images using the sift as a service and the original executable sift software @xcite .
we then introduce an application of the sift algorithm in section 3.4 .
the impact of the siftservice.com is presented in section 3.5 .
all modules , classes , components , and communication packages of siftservice.com were implemented by java se 7 . in the server side ( application server )
, we used a 64-bit linux centos operating system on a virtual server with 2v cores processor , 100 mbits / s bandwidth , and 2 gb of ram . in the client side ( client ) , we employed 64-bit ms windows 8 operating system with 3.00 ghz intel dual core cpu , 2 mb cache , and 4 gb of ram .          in this section
we bring up a validation summary on the accuracy and reliability of the proposed system for feature points detection .
figure 4 shows the accuracy in feature points detection .
the service produces feature points which are very similar to the original one implemented by lowe . the error threshold of the sift as a service is less than 1% in feature points detection .
figure 5 shows the points matching accuracy for images `` hall01 '' and `` hall02 '' form the `` hall '' images set .
we can see that the error threshold for point matching is less than 2% comparing with the david lowe s implementation .
as we mentioned in the abstract , the sift algorithm has made in big advance in many areas of computer vision application .
an example is 3d surface reconstruction , and in particular 3d microscopy vision .
the general pipeline of 3d microscopy vision is presented in figure 6 .
the sift algorithm is used in step 2 of the pipeline . for further details and information on the proposed method ,
please refer to @xcite .
we just recall that in that work @xcite , we employed surf @xcite instead of sift , but other parts would be same .
siftservice.com has been introduced and it is going to be popular for computer vision research community .
figure 7 present the impact of siftservice.com from december 2014 to march 2015 .
the sift algorithm has been widely used in many computer vision applications . having sift as a service would provide application - to - application interaction and makes sift easier for computer vision researchers and students to rapid application development ( rad ) , communicating each other applications by a tiny service available in the internet . in this project , we have designed and implemented the entire components of the system and addressed some experimental results which show that the service offers promising results . the present work is expected to stimulate more interest and draw attentions from the computer vision community to the fast - growing software as a service and the internet of things ( iot ) area . our contribution could be considered as a preliminary step towards the * computer vision as a service*. the service is freely available at * http://siftservice.com * for any academic , educational and research purposes .
leyva , p. , domenech - asensi , g. , garrigos , j. , illade - quinteiro , j. : simplification and hardware implementation of the feature descriptor vector calculation in the sift algorithm .
ieee international conference on field programmable logic and applications ( fpl24th ) .
( 2014 )          pahlavan tafti , a. , kirkpatrick , a. b. , owen , h. a. , yu , z. : 3d microscopy vision using multiple view geometry and differential evolutionary approaches .
the 10th international symposium on visual computing ( isvc ) , lncs 8888 .
141152 ( 2014 )        hatch , r. : saas architecture , adoption and monetization of saas projects using best practice service strategy , service design , service transition , service operation and continual service improvement processes
. emereo pty ltd .
image features detection and description is a longstanding topic in computer vision and pattern recognition areas . 
 the scale invariant feature transform ( sift ) is probably the most popular and widely demanded feature descriptor which facilitates a variety of computer vision applications such as image registration , object tracking , image forgery detection , and 3d surface reconstruction . 
 this work introduces a software as a service ( saas ) based implementation of the sift algorithm which is freely available at * http://siftservice.com * for any academic , educational and research purposes . 
 the service provides application - to - application interaction and aims rapid application development ( rad ) and also fast prototyping for computer vision students and researchers all around the world . 
 an internet connection is all they need !