A robust and low resource FPGA-based stereoscopic vision algorithm

The development of new hardware implementations for stereo vision algorithms requires to establish a balanced trade-off among accuracy, speed and resource consumption requirements. FPGA-based devices allow devising flexible implementations fitting specific applications demands. In this paper, we propose a low resource FPGA-based solution for the calculation of the disparity map in a stereo vision system that employs non-regular shapes for the computation of the aggregate matching. Different strategies for the implementation are evaluated attending to computation speed, precision of the results and hardware demands. In addition, we introduce a new combined cost function for the matching calculation that permits to increase the performance of previous methods in the presence of radiometric changes in both stereo images. The results show that a low resource implementation can be designed with a speed ranging from 2 to 46 frames per second achieving a percentage error in the disparity calculation from 10% to 13%, respectively.

[1]  Kuk-Jin Yoon,et al.  Locally adaptive support-weight approach for visual correspondence search , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[2]  Xing Mei,et al.  On building an accurate stereo matching system on graphics hardware , 2011, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops).

[3]  Boguslaw Cyganek.  Matching of the Multi-channel Images with Improved Nonparametric Transformations and Weighted Binary Distance Measures , 2006, IWCIA.

[4]  Richard Szeliski,et al.  A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms , 2001, International Journal of Computer Vision.

[5]  Rafael Cabeza,et al.  Stereo matching using gradient similarity and locally adaptive support-weight , 2011, Pattern Recognit. Lett..

[6]  Stefania Perri,et al.  Adaptive Census Transform: A novel hardware-oriented stereovision algorithm , 2013, Comput. Vis. Image Underst..

[7]  Tian-Sheuan Chang,et al.  Algorithm and Architecture of Disparity Estimation With Mini-Census Adaptive Support Weight , 2010, IEEE Transactions on Circuits and Systems for Video Technology.

[8]  Gauthier Lafruit,et al.  Cross-Based Local Stereo Matching Using Orthogonal Integral Images , 2009, IEEE Transactions on Circuits and Systems for Video Technology.

[9]  KweonIn So,et al.  Adaptive Support-Weight Approach for Correspondence Search , 2006 .

[10]  Heiko Hirschmüller,et al.  Evaluation of Stereo Matching Costs on Images with Radiometric Differences , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Kristian Ambrosch,et al.  Accurate hardware-based stereo vision , 2010, Comput. Vis. Image Underst..

[12]  Diederik Verkest,et al.  Real-time high-definition stereo matching on FPGA , 2011, FPGA '11.

[13]  Juan A. Gómez-Pulido,et al.  AN FPGA-BASED IMPLEMENTATION FOR MEDIAN FILTER MEETING THE REAL-TIME REQUIREMENTS OF AUTOMATED VISUAL INSPECTION SYSTEMS , 2002 .

[14]  Ruigang Yang,et al.  A Performance Study on Different Cost Aggregation Approaches Used in Real-Time Stereo Matching , 2007, International Journal of Computer Vision.

[15]  Julian Eggert,et al.  A Two-Stage Correlation Method for Stereoscopic Depth Estimation , 2010, 2010 International Conference on Digital Image Computing: Techniques and Applications.

[16]  In-So Kweon,et al.  Adaptive Support-Weight Approach for Correspondence Search , 2006, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  Dah-Jye Lee,et al.  Review of stereo vision algorithms and their suitability for resource-limited systems , 2013, Journal of Real-Time Image Processing.