Occlusion, Attention and Object Representations

Neil Taylor, Christo Panchev, Mathew Hartley, Statis Kasderidis, John G. Taylor

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

5 Citations (Scopus)


Occlusion is currently at the centre of analysis in machine vision. We present an approach to it that uses attention feedback to an occluded object to obtain its correct recognition. Various simulations are performed using a hierarchical visual attention feedback system, based on contrast gain (which we discuss as to its relation to possible hallucinations that could be caused by feedback). We then discuss implications of our results for object representations per se.
Original languageEnglish
Title of host publicationArtificial Neural Networks – ICANN 2006
Subtitle of host publication16th International Conference, Athens, Greece, September 10-14, 2006. Proceedings, Part 1
EditorsStefanos D. Kollias, Andreas Stafylopatis, Włodzisław Duch, Erkki Oja
Place of PublicationBerlin
PublisherSpringer Verlag
Number of pages10
ISBN (Electronic)978-3-540-38627-8
ISBN (Print)978-3-540-38625-4
Publication statusPublished - 2006
Externally publishedYes
Event16th International Conference on Artificial Neural Networks - Athens, Greece
Duration: 10 Sep 200614 Sep 2006
Conference number: 16

Publication series

Name Lecture Notes in Computer Science


Conference16th International Conference on Artificial Neural Networks
Abbreviated titleICANN 2006


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