Pattern Recognition by Cluster Accumulation

Author

Bhatia, Amit ; Bilbro, Griff L. ; Snyder, Wesley E.

Author_Institution

Univ. of California, San Diego, CA, USA

fYear

2010

fDate

21-24 June 2010

Firstpage

799

Lastpage

804

Abstract

When objects in images are small or blurred enough, geometric features are inadequate for reliable pattern recognition. We introduce the Pattern Recognition by Cluster Accumulation (PRCA) method to show that pattern recognition performance can be improved in this situation by using radiometric features for object detection. In addition, PRCA uses clustering to provide feature selection and dimensionality reduction. It uses accumulation to provide robustness against translation, rotation, cluster shape distortion, and inappropriate splitting or merging of clusters. We find that PRCA performs faster than normalized cross correlation and faster than mutual information methods.

Keywords

correlation methods; feature extraction; object detection; pattern clustering; shape recognition; PRCA method; cluster accumulation; cluster shape distortion; dimensionality reduction; feature selection; geometric features; normalized cross correlation; object detection; pattern recognition; radiometric features; Clustering algorithms; Computer vision; Intelligent vehicles; Layout; Lighting; Pattern recognition; Pixel; Radiometry; Robustness; USA Councils; Clustering; Pattern Recognition;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Vehicles Symposium (IV), 2010 IEEE

Conference_Location

San Diego, CA

ISSN

1931-0587

Print_ISBN

978-1-4244-7866-8

Type

conf

DOI

10.1109/IVS.2010.5547958

Filename

5547958