Qualitative depth form stereo, with applications
Computer Vision, Graphics, and Image Processing
Relative Affine Structure: Canonical Model for 3D From 2D Geometry and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Audio-Visual Clustering for 3D Speaker Localization
MLMI '08 Proceedings of the 5th international workshop on Machine Learning for Multimodal Interaction
Detection and localization of 3d audio-visual objects using unsupervised clustering
ICMI '08 Proceedings of the 10th international conference on Multimodal interfaces
Conjugate mixture models for clustering multimodal data
Neural Computation
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Binocular information about the structure of a scene is contained in the relative positions of corresponding points in the two views. If the eyes rotate, in order to fixate a different target, then the disparity at a given image location is likely to change. Quite different disparities can be produced at the same location, as the eyes move from one fixation-point to the next. The pointwise variability of the disparity map is problematic for biological visual systems, in which stereopsis is based on simple, short-range mechanisms. It is argued here that the problem can be addressed in two ways; firstly by an appropriate representation of disparity, and secondly by learning the typical pattern of image correspondences. It is shown that the average spatial structure of the disparity field can be estimated, by integrating over a series of binocular fixations. An algorithm based on this idea is tested on natural images. Finally, it is shown how the average pattern of disparities could help to put the images into binocular correspondence.