Comparing shape and texture features for pattern recognition in simulation data

Shawn D. Newsam; Chandrika Kamath

doi:10.1117/12.587057

1 March 2005 Comparing shape and texture features for pattern recognition in simulation data

Shawn D. Newsam, Chandrika Kamath

Proceedings Volume 5672, Image Processing: Algorithms and Systems IV; (2005) https://doi.org/10.1117/12.587057
Event: Electronic Imaging 2005, 2005, San Jose, California, United States

Abstract

Shape and texture features have been used for some time for pattern recognition in datasets such as remote sensed imagery, medical imagery, photographs, etc. In this paper, we investigate shape and texture features for pattern recognition in simulation data. In particular, we explore which features are suitable for characterizing regions of interest in images resulting from fluid mixing simulations. Three texture features -- gray level co-occurrence matrices, wavelets, and Gabor filters -- and two shape features -- geometric moments and the angular radial transform -- are compared. The features are evaluated using a similarity retrieval framework. Our preliminary results indicate that Gabor filters perform the best among the texture features and the angular radial transform performs the best among the shape features. The feature which performs the best overall is dependent on how the groundtruth dataset is created.

Citation Download Citation

Shawn D. Newsam and Chandrika Kamath "Comparing shape and texture features for pattern recognition in simulation data", Proc. SPIE 5672, Image Processing: Algorithms and Systems IV, (1 March 2005); https://doi.org/10.1117/12.587057

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