Three-Dimensional Feature Detection Using Optimal Steerable Filters

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.


Published in:
Proceedings of the 2005 IEEE International Conference on Image Processing (ICIP'05), Genova, Italian Republic, 1158–1161
Year:
2005
Publisher:
IEEE
Laboratories:




 Record created 2015-09-18, last modified 2018-03-17

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