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Dario Rosas, Volodymyr Ponomaryov, Rogelio Reyes-Reyes


In this study, we present a novel local image descriptor, which is very efficient to compute densely, with semantic information based on visual primitives and relations between them, namely, coplanarity, cocolority, distance and angle. The designed feature descriptor covers both geometric and appearance information. The proposed descriptor has demonstrated its ability to compute dense depth maps from image pairs with a good performance evaluated by the Bad Matched Pixel criterion. Since novel descriptor is very high dimensional, we show that a compact descriptor can be sustitable. An analysis of size reduction was performed in order to reduce the computational complexity with no lose of quality by using different algorithms like max-min or PCA. This novel descriptor has a better results than state-of-the-art methods in stereo vision task. Also, an implementation in GPU hardware is presented performing time reduction using a NVIDIA R GeForce R GT640 graphic card and Matlab over a PC with Windows 10.


Image Local Descriptor; Dense Depth Map; Visual Primitives; Vision Stereo; PCA; GPU.

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