Dario Rosas, Volodymyr Ponomaryov, Rogelio Reyes-Reyes / International Journal of Computing, 17(3) 2018, 171-179 171 PRIMITIVE VISUAL RELATION FEATURE DESCRIPTOR APPLIED TO STEREO VISION Dario Rosas, Volodymyr Ponomaryov, Rogelio Reyes-Reyes SEPI Esime Culhuacan, Instituto Politécnico Nacional, Av. Santa Anna 1000, Mexico City, Mexico, drosasm0701@alumno.ipn.mx, vponomar@ipn.mx, rreyesre@ipn.mx Paper history: Received 23 August 2018 Received in revised form 19 Sep. 2018 Accepted 20 September 2018 Available online 30 September 2018 Keywords: Image Local Descriptor; Dense Depth Map; Visual Primitives; Vision Stereo; PCA; GPU. Abstract: 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. Copyright © Research Institute for Intelligent Computer Systems, 2018. All rights reserved. 1. INTRODUCTION Computer vision is an interdisciplinary field that seeks to perform process as similar to human vision, employing methods that can understand digital images and video, such as acquisition, processing and analyzing. Some tasks in computer vision include segmentation, object detection and identification by extracting high-dimension data from the real word and transforming data using descriptor that can interface with other processes. Stereo vision is one of the most active research areas in the computer vision. Therefore, a variety of solutions and variations of existing methods have been presented for specific needs or requirements. The goal of stereo vision is to estimate the depth of a scene by disparity maps, matching similarities from a pair of images. A taxonomy of existing stereo algorithms that allows the dissection and comparison of individual algorithm components is presented in [1]. This taxonomy is based on four steps that stereo algorithms typically perform: 1. Matching Cost 2. Cost aggregation 3. Disparity computation 4. Disparity refinement The sequence of the steps depends on the type of an algorithm, where local algorithms typically follow the steps 1,2,3 but some others combine steps 1,2 and use matching costs based on the support region. On the other hand, global algorithms do not perform an aggregation step but rather seek a disparity assignment (step 3) that minimizes a global cost function (step 1). Some authors focus their efforts in one or more steps, depending on particulars goals. Difference matching cost have been studied; the most common is based in pixel difference and includes squared intensity differences (SAD) and absolute intensity differences (AD); also, in the video processing field, the mean absolute difference (MAD) and mean- squared error are more frequently used. Other approaches use gradient-based measures and non- parametric measures, such as rank and census transform. It is also possible to perform a preprocessing step, using histogram equalization or Gaussian filters. Local and windows-based methods aggregate the matching cost over a support region employing squared windows or Gaussian convolutions, shiftable windows or windows with adaptive sizes. computing@computingonline.net www.computingonline.net Print ISSN 1727-6209 On-line ISSN 2312-5381 International Journal of Computing