Invariant multiscale triangle feature for shape recognition

Chengzhuan Yang and Qian Yu

Applied Mathematics and Computation, 2021, vol. 403, issue C

Abstract: Shape is an important visual characteristic in representing an object, and it is also an important part of human visual information. Shape recognition is an important research direction in pattern recognition and image understanding. However, it is a difficult problem to extract discriminative and robust shape descriptors in shape recognition. This is because there are very large deformations in the shape, such as geometric changes, intra-class variations, and nonlinear deformations. These factors directly influence the accuracy of shape recognition. To deal with the influence of these factors on the performance of shape recognition and enhance the accuracy of recognition, we present a novel shape description method called invariant multiscale triangle feature (IMTF) for robust shape recognition. This method uses two types of invariant triangle features to obtain the shape features of an object, and it can effectively combine the boundary and the interior characteristics of a shape, and hence can increase the distinguish ability of the shape. We conducted an extensive experimental analysis on some shape benchmarks. The results indicate that our method can achieve high recognition accuracy. The superiority of our method has been further demonstrated in comparison to the state-of-the-art shape descriptors.

Keywords: Shape descriptor; Shape matching; Dynamic programming; Shape recognition (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0096300321001442
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:403:y:2021:i:c:s0096300321001442

DOI: 10.1016/j.amc.2021.126096

Access Statistics for this article

Applied Mathematics and Computation is currently edited by Theodore Simos

More articles in Applied Mathematics and Computation from Elsevier
Bibliographic data for series maintained by Catherine Liu ().