A Performance Study on Different Cost Aggregation Approaches Used in Real-Time Stereo Matching
International Journal of Computer Vision
A Geometric Approach for Regularization of the Data Term in Stereo-Vision
Journal of Mathematical Imaging and Vision
Generation of a disparity map using piecewise linear transformation
CIMMACS'06 Proceedings of the 5th WSEAS International Conference on Computational Intelligence, Man-Machine Systems and Cybernetics
Search Space Reduction for MRF Stereo
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
A convex optimization approach for depth estimation under illumination variation
IEEE Transactions on Image Processing
Local stereo matching using geodesic support weights
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Real time stereo vision using exponential step cost aggregation on GPU
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Similarity measures for image matching despite occlusions in stereo vision
Pattern Recognition
Efficient stereo and optical flow with robust similarity measures
DAGM'11 Proceedings of the 33rd international conference on Pattern recognition
Fast dense stereo matching using adaptive window in hierarchical framework
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part II
SUSAN window based cost calculation for fast stereo matching
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part II
Correspondence search in the presence of specular highlights using specular-free two-band images
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
Temporally consistent disparity and optical flow via efficient spatio-temporal filtering
PSIVT'11 Proceedings of the 5th Pacific Rim conference on Advances in Image and Video Technology - Volume Part I
Extracting 3d scene-consistent object proposals and depth from stereo images
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
Secrets of adaptive support weight techniques for local stereo matching
Computer Vision and Image Understanding
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In this paper, we present a new area-based method for visual correspondence search that focuses on the dissimilarity computation. Local and area-based matching methods generally measure the similarity (or dissimilarity) between the image pixels using local support windows. In this approach, an appropriate support window should be selected adaptively for each pixel to make the measure reliable and certain. Finding the optimal support window with an arbitrary shape and size is, however, very difficult and generally known as an NP-hard problem. For this reason, unlike the existing methods that try to find an optimal support window, we adjusted the support-weight of each pixel in a given support window. The adaptive support-weight of a pixel is computed based on the photometric and geometric relationship with the pixel under consideration. Dissimilarity is then computed using the raw matching costs and support-weights of both support windows, and the correspondence is finally selected by the WTA (Winner-Takes-All) method. The experimental results for the rectified real images show that the proposed method successfully produces piecewise smooth disparity maps while preserving sharp depth discontinuities accurately.