Robust detection of medial-axis by onset synchronization of border-ownership selective cells and shape reconstruction from its medial-axis

Yasuhiro Hatori, Ko Sakai

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

There is little understanding on representation and reconstruction of object shape in the cortex. Physiological studies with macaque suggested that neurons in V1 respond to Medial-Axis (MA). We investigated whether (1) early visual areas could provide basis for MA representation, and (2) we could reconstruct the original shape from its MA, with a physiologically realistic computational model consisting of early- to intermediate-level visual areas. Assuming the synchronization of border-ownership selective cells at stimulus onset, our model was capable of detecting MA, indicating that early visual area could provide basis for MA representation. Furthermore, we propose a physiologically plausible reconstruction algorithm with the summation of distinct gaussians.

Original languageEnglish
Title of host publicationAdvances in Neuro-Information Processing - 15th International Conference, ICONIP 2008, Revised Selected Papers
Pages301-309
Number of pages9
EditionPART 1
DOIs
Publication statusPublished - 2009
Event15th International Conference on Neuro-Information Processing, ICONIP 2008 - Auckland, New Zealand
Duration: 2008 Nov 252008 Nov 28

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume5506 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference15th International Conference on Neuro-Information Processing, ICONIP 2008
Country/TerritoryNew Zealand
CityAuckland
Period08/11/2508/11/28

Fingerprint

Dive into the research topics of 'Robust detection of medial-axis by onset synchronization of border-ownership selective cells and shape reconstruction from its medial-axis'. Together they form a unique fingerprint.

Cite this