Machine learning paradigms for pattern recognition and image understanding

Machine learning paradigms for pattern recognition and image understanding Machine learning paradigms for pattern recognition and image understanding TERRY CAELLI1 and WALTER F. BISCHOF2 1Department of Computer Science, Curtin University of Technology, GPO Box U1987, Perth, WA 6001, Australia 2Department of Psychology, University of Alberta, Edmonton, Alberta, T6G 2E9, Canada Received 12 July 1995; accepted 30 October 1995 Abstract-In this paper some issues are considered related to the encoding of spatial information and associated perceptual learning algorithms which, it is claimed, are necessary for robust pattern and object recognition in multi-object (natural) scenes. The types of learning requirements within a 'recognition-by- parts' paradigm are contrasted with findings from alternative models. 1. INTRODUCTION Over the past decades, an enormous effort has been devoted to determining how biological visual systems decompose the sensed world into salient features or parts. Questions about these processes have varied from very basic problems of 'foreground- background' segregation (for example, Julesz, 1984) to more complex problems of what constitute 'parts' of 3D objects (for example, Hoffman and Richards, 1986; Bie- derman, 1987). In a similar way, most computational models for computer pattern and object recognition are based on image segmentation, feature and part extraction procedures. Structures (patterns, textures, objects) are typically defined by descrip- http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Spatial Vision (continued as Seeing & Perceiving from 2010) Brill

Machine learning paradigms for pattern recognition and image understanding

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Publisher
BRILL
Copyright
© 1996 Koninklijke Brill NV, Leiden, The Netherlands
ISSN
0169-1015
eISSN
1568-5683
D.O.I.
10.1163/156856896X00079
Publisher site
See Article on Publisher Site

Abstract

Machine learning paradigms for pattern recognition and image understanding TERRY CAELLI1 and WALTER F. BISCHOF2 1Department of Computer Science, Curtin University of Technology, GPO Box U1987, Perth, WA 6001, Australia 2Department of Psychology, University of Alberta, Edmonton, Alberta, T6G 2E9, Canada Received 12 July 1995; accepted 30 October 1995 Abstract-In this paper some issues are considered related to the encoding of spatial information and associated perceptual learning algorithms which, it is claimed, are necessary for robust pattern and object recognition in multi-object (natural) scenes. The types of learning requirements within a 'recognition-by- parts' paradigm are contrasted with findings from alternative models. 1. INTRODUCTION Over the past decades, an enormous effort has been devoted to determining how biological visual systems decompose the sensed world into salient features or parts. Questions about these processes have varied from very basic problems of 'foreground- background' segregation (for example, Julesz, 1984) to more complex problems of what constitute 'parts' of 3D objects (for example, Hoffman and Richards, 1986; Bie- derman, 1987). In a similar way, most computational models for computer pattern and object recognition are based on image segmentation, feature and part extraction procedures. Structures (patterns, textures, objects) are typically defined by descrip-

Journal

Spatial Vision (continued as Seeing & Perceiving from 2010)Brill

Published: Jan 1, 1996

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