Below is an abstract from a computer science research paper. Given this abstract, your task is to extract all unique relationships between entities of the following types:

- "USED-FOR": For instance, "Our method models user proficiency" -> ["method", "user proficiency", "USED-FOR"].
- "EVALUATE-FOR": For instance, "We evaluate our method using F1-measure" -> ["F1-measure", "method", "EVALUATE-FOR"].
- "FEATURE-OF": For instance, "prior knowledge of the model" -> ["prior knowledge", "model", "FEATURE-OF"].
- "HYPONYM-OF": For instance, "TUIT is a software library" -> ["TUIT", "software library", "HYPONYM-OF"].
- "PART-OF": For instance, "We incorporate NLU module into the system." -> ["NLU module", "system", "PART-OF"].
- "COMPARE": For instance, "Unlike the quantitative prior, the qualitative prior is often ignored" -> ["quantitative prior", "qualitative prior", "COMPARE"].
- "CONJUNCTION": For instance: "obtained from human expert or knowledge base" -> ["human expert", "knowledge base", "CONJUNCTION"].

Format your output as a json array. Each entry in the array should express a single relation, formatted as ["<Entity_A>", "<Entity_B>", "<RELATION_A_B>"]. An example output might look like:

[["neural networks", "classification", "USED-FOR"], ["neuron", "neural network", "PART-OF"]].

If no relations are found, please return an empty array [].

Abstract:

In this paper, we introduce KAZE features, a novel multiscale 2D feature detection and description algorithm in nonlinear scale spaces. Previous approaches detect and describe features at different scale levels by building or approximating the Gaussian scale space of an image. However, Gaussian blurring does not respect the natural boundaries of objects and smoothes to the same degree both details and noise, reducing localization accuracy and distinctiveness. In contrast, we detect and describe 2D features in a nonlinear scale space by means of nonlinear diffusion filtering. In this way, we can make blurring locally adaptive to the image data, reducing noise but retaining object boundaries, obtaining superior localization accuracy and distinctiviness. The nonlinear scale space is built using efficient Additive Operator Splitting (AOS) techniques and variable con-ductance diffusion. We present an extensive evaluation on benchmark datasets and a practical matching application on deformable surfaces. Even though our features are somewhat more expensive to compute than SURF due to the construction of the nonlinear scale space, but comparable to SIFT, our results reveal a step forward in performance both in detection and description against previous state-of-the-art methods.
[["nonlinear scale space", "KAZE features", "FEATURE-OF"], ["detection", "state-of-the-art methods", "EVALUATE-FOR"], ["Additive Operator Splitting (AOS) techniques", "variable con-ductance diffusion", "CONJUNCTION"], ["KAZE features", "SIFT", "COMPARE"], ["variable con-ductance diffusion", "nonlinear scale space", "USED-FOR"], ["description", "KAZE features", "EVALUATE-FOR"], ["description", "state-of-the-art methods", "EVALUATE-FOR"], ["Additive Operator Splitting (AOS) techniques", "nonlinear scale space", "USED-FOR"], ["nonlinear scale spaces", "multiscale 2D feature detection and description algorithm", "FEATURE-OF"], ["KAZE features", "multiscale 2D feature detection and description algorithm", "HYPONYM-OF"], ["KAZE features", "state-of-the-art methods", "COMPARE"], ["KAZE features", "SURF", "COMPARE"], ["detection", "KAZE features", "EVALUATE-FOR"], ["detection", "description", "CONJUNCTION"], ["nonlinear diffusion filtering", "KAZE features", "USED-FOR"]]