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International Journal of Computer Vision
A maximum likelihood stereo algorithm
Computer Vision and Image Understanding
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Depth Discontinuities by Pixel-to-Pixel Stereo
International Journal of Computer Vision
Disparity-Space Images and Large Occlusion Stereo
ECCV '94 Proceedings of the Third European Conference-Volume II on Computer Vision - Volume II
Depth and motion discontinuities
Depth and motion discontinuities
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ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
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IEEE Computer Graphics and Applications
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International Journal of Computer Vision
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Reconfigurable hardware implementation of a phase-correlation stereoalgorithm
Machine Vision and Applications
Towards 3-D scene reconstruction from broadcast video
Image Communication
Shape recovery from turntable sequence using rim reconstruction
Pattern Recognition
Fusion of stereo vision and Time-Of-Flight imaging for improved 3D estimation
International Journal of Intelligent Systems Technologies and Applications
Coarse-to-fine stereo vision with accurate 3D boundaries
Image and Vision Computing
3D Model Reconstruction from Turntable Sequence with Multiple -View Triangulation
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part II
VoC: a reconfigurable matrix for stereo vision processing
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
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Computer Graphics Forum
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In this paper, a new hierarchical stereo algorithm is presented. The algorithm matches individual pixels in corresponding scanlines by minimizing a cost function. Several cost functions are compared. The algorithm achieves a tremendous gain in speed and memory requirements by implementing it hierarchically. The images are downsampled an optimal number of times and the disparity map of a lower level is used as ‘offset’ disparity map at a higher level. An important contribution consists of the complexity analysis of the algorithm. It is shown that this complexity is independent of the disparityrange. This result is also used to determine the optimal number of downsample levels. This speed gain results in the ability to use more complex (compute intensive) cost functions that deliver high quality disparity maps. Another advantage of this algorithm is that cost functions can be chosen independent of the optimisation algorithm. The algorithm in this paper is symmetric, i.e. exactly the same matches are found if left and right image are swapped. Finally, the algorithm was carefully implemented so that a minimal amount of memory is used. It has proven its efficiency on large images with a high disparity range as well as its quality. Examples are given in this paper.