SPiraL Aggregation Map (SPLAM): A new descriptor for robust template matching with fast algorithm. Issue 5 (May 2015)

• Record Type: Journal Article
• Authors: Shih, Huang-Chia; Yu, Kuan-Chun
• Is Part Of: Pattern recognition. Volume 48:Issue 5(2015:May)
• Page Start: 1707
• Page End: 1723
• Publication Date: 2015-05
• Subjects: Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2014.11.004
• Languages: English
• ISSNs: 0031-3203

Abstract

<abstract abstract-type="author" id="ab0005"> <title id="sect0005">Abstract</title> <sec> <p id="sp0130">This paper describes a robust template matching algorithm undergoing rotation–scaling–translation (RST) variations via our proposed SPiraL Aggregation Map (SPLAM), which is a novel image warping scheme. It not only provides an efficient method for generating the desired projection profiles for matching, it also enables us to determine the rotation angle, and is invariant to scale changes. Compared to other model-based methods, the proposed spiral projection model (SPM) provides the structural and statistical information about the template in a more general and easier to comprehend format. The SPM is a model-based texture-description scheme that enables the simultaneous representation for each value of projection profile. The profile, a set of parametric projection values functions by angular indexing, is the aggregate from a group of spiral sampling pixels. The experimental evaluation shows that the properties of the algorithm achieved very attractive results.</p> </sec> </abstract>