DC FieldValueLanguage
dc.contributor.authorNtalianis, Klimis-
dc.contributor.authorRaftopoulos, Konstantinos A.-
dc.contributor.authorPapadakis, Nikos-
dc.date.accessioned2024-11-01T09:22:54Z-
dc.date.available2024-11-01T09:22:54Z-
dc.date.issued2006-01-01-
dc.identifierscopus-33749857313-
dc.identifier.isbn978-3-540-38625-4-
dc.identifier.isbn978-3-540-38627-8-
dc.identifier.issn1611-3349-
dc.identifier.other33749857313-
dc.identifier.urihttps://uniwacris.uniwa.gr/handle/3000/2855-
dc.description.abstractWe 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.en_US
dc.language.isoenen_US
dc.relation.ispartofArtificial Neural Networks - ICANN 2006en_US
dc.relation.ispartofseriesLecture Notes in Computer Scienceen_US
dc.subjectCurvature detectionen_US
dc.subjectLandmark pointsen_US
dc.subjectShape encodingen_US
dc.subjectVisual cortexen_US
dc.titleVisual pathways for detection of landmark pointsen_US
dc.typeConference Paperen_US
dc.relation.conference16th International Conference on Artificial Neural Networks (ICANN 2006), 10-14 September 2006, Athens, Greeceen_US
dc.identifier.doi10.1007/11840817_76en_US
dc.identifier.scopus2-s2.0-33749857313-
dcterms.accessRights0en_US
dc.relation.deptDepartment of Business Administrationen_US
dc.relation.facultySchool of Administrative, Economics and Social Sciencesen_US
dc.identifier.spage728en_US
dc.identifier.epage739en_US
dc.collaborationUniversity of West Attica (UNIWA)en_US
dc.journalsOpen Accessen_US
dc.publicationPeer Revieweden_US
dc.countryGreeceen_US
local.metadatastatusnot verifieden_US
item.cerifentitytypePublications-
item.languageiso639-1en-
item.openairetypeConference Paper-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.fulltextNo Fulltext-
item.grantfulltextnone-
crisitem.author.deptDepartment of Business Administration-
crisitem.author.facultySchool of Administrative, Economics and Social Sciences-
crisitem.author.parentorgSchool of Administrative, Economics and Social Sciences-
Appears in Collections:Book Chapter / Κεφάλαιο Βιβλίου
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