Random Binary Local Patch Clustering Transforms Based Image Matching for Nonlinear Intensity Changes. (19th September 2018)

• Record Type: Journal Article
• Authors: Wang, Han; Xu, Zhihuo; Ko, Hanseok
• Other Names: Karamanos Konstantinos Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2018(2018)
• Publication Date: 2018-09-19
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2018/6360741
• Languages: English
• ISSNs: 1024-123X

Abstract

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.