Texture analysis and segmentation using modulation features, generative models, and weighted curve evolution

Kokkinos, I; Evangelopoulos, G; Maragos, P

dc.contributor.author	Kokkinos, I	en
dc.contributor.author	Evangelopoulos, G	en
dc.contributor.author	Maragos, P	en
dc.date.accessioned	2014-03-01T01:32:02Z
dc.date.available	2014-03-01T01:32:02Z
dc.date.issued	2009	en
dc.identifier.issn	0162-8828	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/20032
dc.subject	AM-FM models	en
dc.subject	Cue combination	en
dc.subject	Curve evolution	en
dc.subject	Demodulation	en
dc.subject	Edge and feature detection	en
dc.subject	Generative models	en
dc.subject	Image models	en
dc.subject	Image processing and computer vision	en
dc.subject	Image segmentation	en
dc.subject	Segmentation	en
dc.subject	Statistical	en
dc.subject	Texture	en
dc.subject	Texture analysis	en
dc.subject.classification	Computer Science, Artificial Intelligence	en
dc.subject.classification	Engineering, Electrical & Electronic	en
dc.subject.other	Amplitude modulation	en
dc.subject.other	Computer vision	en
dc.subject.other	Demodulation	en
dc.subject.other	Digital image storage	en
dc.subject.other	Feature extraction	en
dc.subject.other	Image analysis	en
dc.subject.other	Image enhancement	en
dc.subject.other	Image processing	en
dc.subject.other	Image retrieval	en
dc.subject.other	Image segmentation	en
dc.subject.other	Modulation	en
dc.subject.other	Optical variables measurement	en
dc.subject.other	Probability	en
dc.subject.other	Textures	en
dc.subject.other	AM-FM models	en
dc.subject.other	Cue combination	en
dc.subject.other	Curve evolution	en
dc.subject.other	Edge and feature detection	en
dc.subject.other	Generative models	en
dc.subject.other	Image models	en
dc.subject.other	Segmentation	en
dc.subject.other	Statistical	en
dc.subject.other	Texture analysis	en
dc.subject.other	Edge detection	en
dc.subject.other	algorithm	en
dc.subject.other	article	en
dc.subject.other	artificial intelligence	en
dc.subject.other	automated pattern recognition	en
dc.subject.other	computer assisted diagnosis	en
dc.subject.other	computer simulation	en
dc.subject.other	methodology	en
dc.subject.other	statistical model	en
dc.subject.other	three dimensional imaging	en
dc.subject.other	Algorithms	en
dc.subject.other	Artificial Intelligence	en
dc.subject.other	Computer Simulation	en
dc.subject.other	Image Interpretation, Computer-Assisted	en
dc.subject.other	Imaging, Three-Dimensional	en
dc.subject.other	Models, Statistical	en
dc.subject.other	Pattern Recognition, Automated	en
dc.title	Texture analysis and segmentation using modulation features, generative models, and weighted curve evolution	en
heal.type	journalArticle	en
heal.identifier.primary	10.1109/TPAMI.2008.33	en
heal.identifier.secondary	http://dx.doi.org/10.1109/TPAMI.2008.33	en
heal.language	English	en
heal.publicationDate	2009	en
heal.abstract	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. © 2009 IEEE.	en
heal.publisher	IEEE COMPUTER SOC	en
heal.journalName	IEEE Transactions on Pattern Analysis and Machine Intelligence	en
dc.identifier.doi	10.1109/TPAMI.2008.33	en
dc.identifier.isi	ISI:000260889700012	en
dc.identifier.volume	31	en
dc.identifier.issue	1	en
dc.identifier.spage	142	en
dc.identifier.epage	157	en