Trace Inference, Curvature Consistency, and Curve Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Two stages of curve detection suggest two styles of visual computation
Neural Computation
Stereo by Incremental Matching of Contours
IEEE Transactions on Pattern Analysis and Machine Intelligence
A neural model of contour integration in the primary visual cortex
Neural Computation
Logical/Linear Operators for Image Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
Indexing visual representations through the complexity map
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Depth from edge and intensity based stereo
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
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In stereoscopic images, the behavior of a curve in space is related to the appearance of the curve in the left and right image planes. Formally, this relationship is governed by the projective geometry induced by the stereo camera configuration and by the differential structure of the curve in the scene. We propose that the correspondence problem-matching corresponding points in the image planes- can be solved by relating the differential structure in the left and right image planes to the geometry of curves in space. Specifically, the compatibility between two pairs of corresponding points and tangents at those points is related to the local approximation of a space curve using an osculating helix. This builds upon earlier work on co-circularity, or the transport of tangents along the osculating circle. To guarantee robustness against small changes in the camera parameters, we select a specific osculating helix. A relaxation labeling network demonstrates that the compatibilities can be used to infer the appropriate correspondences in a scene. Examples on which standard approaches fail are demonstrated.