Attribute openings, thinnings, and granulometries
Computer Vision and Image Understanding
Morphological Image Analysis: Principles and Applications
Morphological Image Analysis: Principles and Applications
Granulometric Analysis of Document Images
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Frontal-view gait recognition by intra- and inter-frame rectangle size distribution
Pattern Recognition Letters
Robust analysis of silhouettes by morphological size distributions
ACIVS'06 Proceedings of the 8th international conference on Advanced Concepts For Intelligent Vision Systems
Flat zones filtering, connected operators, and filters by reconstruction
IEEE Transactions on Image Processing
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Most morphological operators use a unique structuring element, possibly at different scales, to describe an object. In addition, morphological algorithms are often restricted to 1D structuring elements, combinations of 1D elements, or isotropic structuring elements (like circles), because of the lack of methods directly applicable to arbitrary shaped 2D structuring elements. While these descriptors have proved useful in the past, we propose an alternative that uses the list of maximal rectangles contained in a set X. In particular, we focus on an opening that preserves large rectangles contained in a set X and on its companion 2D algorithm that builds a list of all the maximal rectangles that fit inside an arbitrary set X. This list is the base of new descriptors that have been used successfully for machine learning tasks related to the analysis of human silhouettes. For convenience, we provide the C source code and a program of our algorithm at http://www.ulg.ac.be/telecom/rectangles