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Abstract

A neurally-inspired 3-D visual object recognition system is described called SEEMORE, whose goal is to reliably identify common objects from a large known set — independent of viewing angle, distance, and partial occlusion. Three distinctive features of this view-based approach are: (1) use of a shallow but very high- dimensional feedforward network of simple filtering operations, (2) simultaneous use of cues from several different visual sub-modalities, i.e. color, shape, texture, etc., and (3) inclusion of objects that are rigid, non-rigid, articulated, and/or statistical in nature. Preliminary results have been obtained using combinations of color and shape-based features. In response to a testset of 150 novel views of 50 known objects presented individually in color video images, SEEMORE currently identifies the object correctly 83% of the time (chance is 2%). Six non-rigid objects, including a telphone cord, a bicycle chain, two scarves, a grape cluster, and a maple-leaf cluster were included in the training set; all 18 novel test views of these objects were correctly recognized. Recognition time on a Sparc-2 is a non-optimized 60 seconds including all image processing. Extension to the case of multiple object scenes is discussed.

Keywords

Feature Channel Partial Occlusion Object View Shape Channel Visual Object Recognition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media Dordrecht 2000

Authors and Affiliations

  • Bartlett W. Mel
    • 1
  1. 1.University of Southern CaliforniaLos AngelesUSA

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