From orientation selection to optical flow
Computer Vision, Graphics, and Image Processing - Special issue on human and machine vission, part II
The Frequency Structure of One-Dimensional Occluding Image Signals
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distributional population codes and multiple motion models
Proceedings of the 1998 conference on Advances in neural information processing systems II
Measurement of Image Velocity
Introductory Techniques for 3-D Computer Vision
Introductory Techniques for 3-D Computer Vision
Optical Snow and the Aperture Problem
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
International Journal of Computer Vision
On the computation of image motion and heading in a 3-D cluttered scene
Optic flow and beyond
On the computation of image motion and heading in a 3-D cluttered scene
Optic flow and beyond
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Optical snow is a natural type of image motion that results when the observer moves laterally relative to a cluttered 3D scene. An example is an observer moving past a bush or through a forest, or a stationary observer viewing falling snow. Optical snow motion is unlike standard motion models in computer vision, such as optical flow or layered motion since such models are based on spatial continuity assumptions. For optical snow, spatial continuity cannot be assumed because the motion is characterized by dense depth discontinuities. In previous work, we considered the special case of parallel optical snow. Here we generalize that model to allow for non-parallel optical snow. The new model describes a situation in which a laterally moving observer tracks an isolated moving object in an otherwise static 3D cluttered scene. We argue that despite the complexity of the motion, sufficient constraints remain that allow such an observer to navigate through the scene while tracking a moving object.