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Random Binary Local Patch Clustering Transforms Based Image Matching for Nonlinear Intensity Changes.
- Source :
- Mathematical Problems in Engineering; 9/19/2018, p1-16, 16p
- Publication Year :
- 2018
-
Abstract
- This paper presents a new feature descriptor that is suitable for image matching under nonlinear intensity changes. The proposed approach consists of the following three steps. First, a binary local patch clustering transform response is employed as the transform space. The value of the new space exhibits a high similarity after changes in intensity. Then, a random binary pattern coding method extracts raw feature histograms from the new space. Finally, the discrimination of the proposed feature descriptor is enhanced by using a multiple spatial support region-based binning method. Experimental results show that the proposed method is able to provide a more robust image matching performance under nonlinear intensity changes. [ABSTRACT FROM AUTHOR]
- Subjects :
- IMAGE registration
HISTOGRAMS
ALGORITHMS
DISTANCES
DATA
Subjects
Details
- Language :
- English
- ISSN :
- 1024123X
- Database :
- Complementary Index
- Journal :
- Mathematical Problems in Engineering
- Publication Type :
- Academic Journal
- Accession number :
- 131845494
- Full Text :
- https://doi.org/10.1155/2018/6360741