We present a framework for feature detection in 3-D using steerable filters. These filters can be designed to optimally respond to a particular type of feature by maximizing several Canny-like criteria. The detection process involves the analytical computation of the orientation and corresponding response of the template. A post-processing step consisting of the suppression of non-maximal values followed by thresholding to eliminate insignificant features concludes the detection procedure. We illustrate the approach with the design of feature templates for the detection of surfaces and curves, and demonstrate their efficiency with practical applications.
Three-dimensional feature detection using optimal steerable filters
IEEE International Conference on Image Processing 2005 ; 2 ; II-1158
2005-01-01
369883 byte
Conference paper
Electronic Resource
English
Three-Dimensional Feature Detection using Optimal Steerable Filters
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