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-
Spatial Vision (continued as Seeing & Perceiving from 2010) – Brill
Published: Jan 1, 1996
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