A Biologically Motivated and Computationally Tractable Model of Low and Mid-Level Vision Tasks

Kokkinos, I; Deriche, R; Maragos, P; Faugeras, O

dc.contributor.author	Kokkinos, I	en
dc.contributor.author	Deriche, R	en
dc.contributor.author	Maragos, P	en
dc.contributor.author	Faugeras, O	en
dc.date.accessioned	2014-03-01T01:19:42Z
dc.date.available	2014-03-01T01:19:42Z
dc.date.issued	2004	en
dc.identifier.issn	0302-9743	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/15677
dc.subject	Computer Vision	en
dc.subject	Cost Function	en
dc.subject	Model Integration	en
dc.subject	Variational Approach	en
dc.subject.classification	Computer Science, Theory & Methods	en
dc.subject.other	PRIMARY VISUAL-CORTEX	en
dc.subject.other	ANISOTROPIC DIFFUSION	en
dc.subject.other	NEURAL DYNAMICS	en
dc.subject.other	BOUNDARY	en
dc.subject.other	SEGMENTATION	en
dc.subject.other	PERCEPTION	en
dc.subject.other	NEURONS	en
dc.subject.other	SYSTEM	en
dc.subject.other	SHAPE	en
dc.title	A Biologically Motivated and Computationally Tractable Model of Low and Mid-Level Vision Tasks	en
heal.type	journalArticle	en
heal.identifier.primary	10.1007/978-3-540-24671-8_40	en
heal.identifier.secondary	http://dx.doi.org/10.1007/978-3-540-24671-8_40	en
heal.language	English	en
heal.publicationDate	2004	en
heal.abstract	This paper presents a biologically motivated model for low and mid-level vision tasks and its interpretation in computer vision terms. Initially we briefly present the biologically plausible model of image segmentation developed by Stephen Grossberg and his collaborators during the last two decades, that has served as the backbone of many researchers' work. Subsequently we describe a novel version of this model with a simpler architecture but superior performance to the original system using nonlinear recurrent neural dynamics. This model integrates multi-scale contour, surface and saliency information in an efficient way, and results in smooth surfaces and thin edge maps, without posterior edge thinning or some sophisticated thresholding process. When applied to both synthetic and true images it gives satisfactory results, favorably comparable to those of classical computer vision algorithms. Analogies between the functions performed by this system and commonly used techniques for low- and mid-level computer vision tasks are presented. Further, by interpreting the network as minimizing a cost functional, links with the variational approach to computer vision are established. © Springer-Verlag 2004.	en
heal.publisher	SPRINGER-VERLAG BERLIN	en
heal.journalName	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en
heal.bookName	LECTURE NOTES IN COMPUTER SCIENCE	en
dc.identifier.doi	10.1007/978-3-540-24671-8_40	en
dc.identifier.isi	ISI:000221519200040	en
dc.identifier.volume	3022	en
dc.identifier.spage	506	en
dc.identifier.epage	517	en