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Object Categorization Object Categorization
Computer and Human Vision Perspectives
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16 - Visual Object Discovery

Published online by Cambridge University Press:  20 May 2010

Sven J. Dickinson
Affiliation:
University of Toronto
Aleš Leonardis
Affiliation:
University of Ljubljana
Bernt Schiele
Affiliation:
Technische Universität, Darmstadt, Germany
Michael J. Tarr
Affiliation:
Carnegie Mellon University, Pennsylvania
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Summary

The Problem of Object Discovery

It is perhaps not inaccurate to say that much of the brain's machinery is, in essence, devoted to the detection of repetitions in the environment. Knowing that a particular entity is the same as the one seen on a previous occasion allows the organism to put into play a response appropriate to the reward contingencies associated with that entity. This entity can take many forms. It can be a temporally extended event, such as an acoustic phrase or a dance move, a location, such as a living room, or an individual object, such as a peacock or a lamp. Irrespective of what the precise entity is, the basic informationprocessing problem is the same – to discover how the complex sensory input can be carved into distinct entities and to recognize them on subsequent occasions. In this chapter, we examine different pieces of knowledge regarding visual object discovery and attempt to synthesize them into a coherent framework.

Let us examine the challenges inherent in this problem a little more closely. Consider Figure 16.1(a). This complex landscape appears to be just a random collection of peaks and valleys, with no clearly defined groups. Yet it represents an image that is easily parsed into distinct objects by the human visual system. Figure 16.1(b) shows the image that corresponds exactly to the plot shown in Figure 16.1(a), which simply represents pixel luminance by height. It is now trivial for us to notice that there are three children, and we can accurately delimit their extents as shown in Figure 16.1(c).

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Object Categorization
Computer and Human Vision Perspectives
, pp. 301 - 323
Publisher: Cambridge University Press
Print publication year: 2009

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