RIA: Deformable Kernel Filtering for Early Visual Processing

点击翻译按钮获取中文摘要

In the first step of visual analysis, early vision, simple image properties such as brightness, color, texture, stereoscopic disparity, motion patterns are analyzed and boundaries, lines, and other salient visual structures of the image are measured and extracted. A number of theoretical and empirical arguments point to the possibility that all early vision tasks may be accomplished by algorithms sharing a common computational structure: convolution with kernels of different orientations, scales, and shapes followed by simple quasi-local nonlinear operations. Such kernals may be generated as deformations (rotations, scalings stretchings) of a template kernel which is synthesized from task specifications. In the last two years a method based on singular value decomposition (SVD) has been proposed to make such continuous-parameter filtering feasible- it has been demonstrated for rotations and scaling in 2 dimensions. This research endeavours to(1) demonstrate the method in new situations including 3D rotations and scalings stretchings and changes of curvature, (2) apply the method to generating filters for various early vision tasks including texture and motion analysis, (3) explore new early vision algorithms made possible by continuous-parameter filtering, (4) understand the connections between filter-design techniques and the SVD method. //