Deformable kernels for early vision - Computer Vision and Pattern Recognition, 1991. Proceedings CVPR '91., IEEE Computer Society Confer
نویسنده
چکیده
Earl~ vision algorithms often have a first stage of linearfiltenng that 'extracts' from the image information at multiple scales of resolution and multiple orientations. A common difficulty in the design and implementation of such schemes is that one feels compelled to discretize coarsely the space of scales and orientations in order to reduce compu~tion ~d storage costs. This discretization produces an1~otrop1es due to a loss of traslation-, rotationscalinginvanance that makes early vision algorithms less precise and more difficult to design. This need not be so: one can compute and store efficiently the response of families of linear filters defined on a continuum of orientations and scales. A tech~iqu~ is presen~ that allows ( 1) to compute the best approximation of a g1ven family using linear combinations of a small number of 'basis' functions; (2) to describe all finitedimensional families, i.e. the families of filters for which a finite dimensional representation is possible with no error. The techniq~e is g~neral. and can be applied to generating filters m arb1trary dimens10ns. Experimental results are presented that demonstrate the applicabilityof the technique to generating multi-orientation multi-scale 20 edge-detection kernels. The implementation issues are also discussed.
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تاریخ انتشار 1998