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Feature Extraction Without Edge Detection

Author(s)
Chaney, Ronald D.
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Abstract
Information representation is a critical issue in machine vision. The representation strategy in the primitive stages of a vision system has enormous implications for the performance in subsequent stages. Existing feature extraction paradigms, like edge detection, provide sparse and unreliable representations of the image information. In this thesis, we propose a novel feature extraction paradigm. The features consist of salient, simple parts of regions bounded by zero-crossings. The features are dense, stable, and robust. The primary advantage of the features is that they have abstract geometric attributes pertaining to their size and shape. To demonstrate the utility of the feature extraction paradigm, we apply it to passive navigation. We argue that the paradigm is applicable to other early vision problems.
Date issued
1993-09-01
URI
http://hdl.handle.net/1721.1/6794
Other identifiers
AITR-1434
Series/Report no.
AITR-1434
Keywords
feature extraction, structure from motion, edge detection, spassive navigation

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