Journal article
Image registration and object recognition using affine invariants and convex hulls
IEEE transactions on image processing, v 8(7), pp 934-946
01 Jan 1999
PMID: 18267506
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
This paper is concerned with the problem of feature point registration and scene recognition from images under weak perspective transformations which are well approximated by affine transformations and under possible occlusion and/or appearance of new objects. It presents a set of local absolute affine invariants derived from the convex hull of scattered feature points (e.g., fiducial or marking points, corner points, inflection points, etc.) extracted from the image. The affine invariants are constructed from the areas of the triangles formed by connecting three vertices among a set of four consecutive vertices (quadruplets) of the convex hull, and hence do make direct use of the area invariance property associated with the affine transformation. Because they are locally constructed, they are very well suited to handle the occlusion and/or appearance of new objects. These invariants are used to establish the correspondences between the convex hull vertices of a test image with a reference image in order to undo the affine transformation between them. A point matching approach for recognition follows this. The time complexity for registering L feature points on the test image with N feature points of the reference image is of order O(NL). The method has been tested on real indoor and outdoor images and performs well
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Details
- Title
- Image registration and object recognition using affine invariants and convex hulls
- Creators
- Zhengwei Yang - KLAF Cohen - Drexel University
- Publication Details
- IEEE transactions on image processing, v 8(7), pp 934-946
- Publisher
- The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:000081151500006
- Scopus ID
- 2-s2.0-0032688130
- Other Identifier
- 991019168311104721
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- Collaboration types
- Industry collaboration
- Domestic collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Engineering, Electrical & Electronic