Visual pathways for detection of landmark points

Raftopoulos, K; Papadakis, N; Ntalianis, K

dc.contributor.author	Raftopoulos, K	en
dc.contributor.author	Papadakis, N	en
dc.contributor.author	Ntalianis, K	en
dc.date.accessioned	2014-03-01T02:44:22Z
dc.date.available	2014-03-01T02:44:22Z
dc.date.issued	2006	en
dc.identifier.issn	0302-9743	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/31776
dc.subject	Curvature detection	en
dc.subject	Landmark points	en
dc.subject	Shape encoding	en
dc.subject	Visual cortex	en
dc.subject.classification	Computer Science, Theory & Methods	en
dc.subject.other	Curvature detection	en
dc.subject.other	Landmark points	en
dc.subject.other	Shape encoding	en
dc.subject.other	Visual cortex	en
dc.subject.other	Cell culture	en
dc.subject.other	Computer hardware	en
dc.subject.other	Mathematical models	en
dc.subject.other	Multilayer neural networks	en
dc.title	Visual pathways for detection of landmark points	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1007/11840817_76	en
heal.identifier.secondary	http://dx.doi.org/10.1007/11840817_76	en
heal.language	English	en
heal.publicationDate	2006	en
heal.abstract	We describe a neuron multi-layered architecture that extracts landmark points of high curvature from 2d shapes and resembles the visual pathway of primates. We demonstrate how the rotated orientation specific receptive fields of the simple neurons that were discovered by Hubel and Wiesel can perform landmark point detection on the 2d contour of the shape that is projected on the retina of the eye. Detection of landmark points of high curvature is a trivial task with sophisticated machine equipment but we demonstrate how such a task can be accomplished by only using the hardware of the visual cortex of primates abiding to the discoveries of Hubel and Wiesel regarding the rotated arrangements of orientation specific simple neurons. The proposed layered architecture first extracts the 2dimensional shape from the projection on the retina then it rotates the extracted shape in multiple layers in order to detect the landmark points. Since rotating the image about the focal origin is equivalent to the rotation of the simple cells orientation field, our model offers an explanation regarding the mystery of the arrangement of the cortical cells in the areas of layer 2 and 3 on the basis of shape cognition from its landmark points. © Springer-Verlag Berlin Heidelberg 2006.	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	10.1007/11840817_76	en
dc.identifier.isi	ISI:000241472100076	en
dc.identifier.volume	4131 LNCS - I	en
dc.identifier.spage	728	en
dc.identifier.epage	739	en