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Low level visual motion processing using local motion detectors

Conference Paper


Abstract


  • This paper explores the importance of cognitively impenetrable processing in biological visual systems, in particular low level processing of motion information. Three visual tasks involving motion processing that are critical to living creatures have been identified, namely the detection of direction of heading, estimation of time to contact and image segmentation. The basis for motion processing in biological systems appears to be local motion detectors. The performance of a variety of biologically inspired motion detection models are therefore examined and compared. Some important issues for further processing of local motion information to solve the three visual tasks are also discussed.

Publication Date


  • 1995

Citation


  • Beare, R., Blanksby, A., & Bouzerdoum, A. (1995). Low level visual motion processing using local motion detectors. In IEEE International Conference on Neural Networks - Conference Proceedings Vol. 1 (pp. 1-6).

Scopus Eid


  • 2-s2.0-0029534534

Start Page


  • 1

End Page


  • 6

Volume


  • 1

Abstract


  • This paper explores the importance of cognitively impenetrable processing in biological visual systems, in particular low level processing of motion information. Three visual tasks involving motion processing that are critical to living creatures have been identified, namely the detection of direction of heading, estimation of time to contact and image segmentation. The basis for motion processing in biological systems appears to be local motion detectors. The performance of a variety of biologically inspired motion detection models are therefore examined and compared. Some important issues for further processing of local motion information to solve the three visual tasks are also discussed.

Publication Date


  • 1995

Citation


  • Beare, R., Blanksby, A., & Bouzerdoum, A. (1995). Low level visual motion processing using local motion detectors. In IEEE International Conference on Neural Networks - Conference Proceedings Vol. 1 (pp. 1-6).

Scopus Eid


  • 2-s2.0-0029534534

Start Page


  • 1

End Page


  • 6

Volume


  • 1