Evaluation of disparity map computed using local stereo parametric and Non-Parametric methods

Vision is the most important sense for humans and because of this human vision system we are able to see the 3D world around us with great clarity and are able to find out depth of each and every object. Many Active and Passive depth estimation techniques have been proposed which are capable of estimating depth of real world scene among which one of the passive method, stereo vision has been proven to provide remarkable results. In this paper different algorithms for estimating reliable and accurate correspondence match for stereoscopic image pairs is presented, which is based on correlation techniques. By taking neighboring disparity values into account, reliability and accuracy of the estimated disparity values are increased. In this paper we present a comparison between different stereo correspondence matching algorithms like SAD, SSD, NCC, Non Parametric census transform & SAD by derivatives and analyze the best match to ground truth images taken from Middlebury online dataset using RMS error and BAD PIXEL match as quality metrics.

[1]  R. T. Gray,et al.  Prediction Of Correlation Errors In Stereo-Pair Images , 1980 .

[2]  Jia Yunde,et al.  Trinocular Cooperative Stereo Vision and Occlusion Detection , 2006, 2006 IEEE International Conference on Robotics and Biomimetics.

[3]  Sebastian Thrun,et al.  High-quality scanning using time-of-flight depth superresolution , 2008, 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[4]  S. Scherer,et al.  Adaptive shape from focus with an error estimation in light microscopy , 2001, ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat..

[5]  D Marr,et al.  Cooperative computation of stereo disparity. , 1976, Science.

[6]  Ramin Zabih,et al.  Non-parametric Local Transforms for Computing Visual Correspondence , 1994, ECCV.

[7]  Antonios Gasteratos,et al.  Obtaining Reliable Depth Maps for Robotic Applications from a Quad-Camera System , 2009, ICIRA.

[8]  T. S. Douglas,et al.  Ultrasound image matching using genetic algorithms , 2006, Medical and Biological Engineering and Computing.

[9]  Takeo Kanade,et al.  Development of a video-rate stereo machine , 1995, Proceedings 1995 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human Robot Interaction and Cooperative Robots.

[10]  Marsha Jo Hannah,et al.  Computer matching of areas in stereo images. , 1974 .

[11]  Gérard G. Medioni,et al.  Interactive 3D model extraction from a single image , 2001, Image Vis. Comput..

[12]  Luc Van Gool,et al.  Simultaneous Segmentation and 3D Reconstruction of Monocular Image Sequences , 2007, 2007 IEEE 11th International Conference on Computer Vision.