Presentation 1997/11/17
Motion Perception Modeled by Fuzzy Hough Transform
Masayuki OKADA, Kiichi URAHAMA,
PDF Download Page PDF download Page Link
Abstract(in Japanese) (See Japanese page)
Abstract(in English) A neural network model is presented for motion perception on the basis of the fuzzy Hough transform. Motion data are extracted at first locally by simple correlation between two time frames of images. Extracted local motion data are next locally integrated by neurons with the Gaussian receptive field, and the maximum likelihood motion vector is estimated by winner-takes-all networks. These locally estimated motion data are then integrated globally through cooperative interaction between the WTA neurons. This global spatial integration solves the aperture problems and at the same time preserves motion boundaries. This model is examined for some elementary examples of the barber pole illusion and psychological experimental results are reproduced qualitatively.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) motion perception / barber pole illusion / fuzzy Hough transform / maximum likelihood estimation
Paper # NC97-44
Date of Issue

Conference Information
Committee NC
Conference Date 1997/11/17(1days)
Place (in Japanese) (See Japanese page)
Place (in English)
Topics (in Japanese) (See Japanese page)
Topics (in English)
Chair
Vice Chair
Secretary
Assistant

Paper Information
Registration To Neurocomputing (NC)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Motion Perception Modeled by Fuzzy Hough Transform
Sub Title (in English)
Keyword(1) motion perception
Keyword(2) barber pole illusion
Keyword(3) fuzzy Hough transform
Keyword(4) maximum likelihood estimation
1st Author's Name Masayuki OKADA
1st Author's Affiliation Kyushu Institute of Design()
2nd Author's Name Kiichi URAHAMA
2nd Author's Affiliation Kyushu Institute of Design
Date 1997/11/17
Paper # NC97-44
Volume (vol) vol.97
Number (no) 379
Page pp.pp.-
#Pages 8
Date of Issue