Comparing Shape and Texture Features for Pattern Recognition in Simulation Data
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.
- Research Organization:
- Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
- Sponsoring Organization:
- US Department of Energy (US)
- DOE Contract Number:
- W-7405-ENG-48
- OSTI ID:
- 15011533
- Report Number(s):
- UCRL-CONF-208568; TRN: US200507%%516
- Resource Relation:
- Journal Volume: 5672; Conference: Presented at: IS&T/SPIE's Annual Symposium on Electronic Imaging, San Jose, CA (US), 01/16/2005--01/20/2005; Other Information: PBD: 10 Dec 2004
- Country of Publication:
- United States
- Language:
- English
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