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Three-dimensional feature detection using optimal steerable filters
Conference proceeding   Open access

Three-dimensional feature detection using optimal steerable filters

F Aguet, M Jacob and M Unser
IEEE International Conference on Image Processing 2005, Vol.2, pp.II-1158
2005
DOI: 10.1109/ICIP.2005.1530266
url
https://doi.org/10.1109/ICIP.2005.1530266View
Published (Version of record) Open Access

Abstract

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.
Jacobian matrices Computer vision Filtering Convolution Nonlinear filters Detectors Eigenvalues and eigenfunctions Polynomials Biomedical imaging Isosurfaces

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