Texture Analysis and Segmentation Using Modulation Features, Generative Models, and Weighted Curve Evolution
Issue No. 01 - January (2009 vol. 31)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TPAMI.2008.33
Iasonas Kokkinos , Ecole Centrale Paris, France
Georgios Evangelopoulos , National Technical University of Athens, Athens
Petros Maragos , National Technical University of Athens, Athens
In this work we approach the analysis and segmentation of natural textured images by combining ideas from image analysis and probabilistic modeling. We rely on AM-FM texture models and specifically on the Dominant Component Analysis (DCA) paradigm for feature extraction. This method provides a low-dimensional, dense and smooth descriptor, capturing essential aspects of texture, namely scale, orientation, and contrast. Our contributions are at three levels of the texture analysis and segmentation problems: First, at the feature extraction stage we propose a Regularized Demodulation Algorithm that provides more robust texture features and explore the merits of modifying the channel selection criterion of DCA. Second, we propose a probabilistic interpretation of DCA and Gabor filtering in general, in terms of Local Generative Models. Extending this point of view to edge detection facilitates the estimation of posterior probabilities for the edge and texture classes. Third, we propose the Weighted Curve Evolution scheme that enhances the Region Competition/ Geodesic Active Regions methods by allowing for the locally adaptive fusion of heterogeneous cues. Our segmentation results are evaluated on the Berkeley Segmentation Benchmark, and compare favorably to current state-of-the-art methods.
Image Processing and Computer Vision, Texture, Edge and feature detection, Segmentation, Image models, Statistical
G. Evangelopoulos, I. Kokkinos and P. Maragos, "Texture Analysis and Segmentation Using Modulation Features, Generative Models, and Weighted Curve Evolution," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 31, no. , pp. 142-157, 2008.