Edge comparison in a digital image

Timothy P. Donovan; Russell E Zuck

doi:10.1117/12.602475

25 May 2005 Edge comparison in a digital image

Timothy P. Donovan, Russell E Zuck

Proceedings Volume 5817, Visual Information Processing XIV; (2005) https://doi.org/10.1117/12.602475
Event: Defense and Security, 2005, Orlando, Florida, United States

Abstract

The purpose of this paper is to study the relationship between a bit map digital image and a given object, called the search object. In particular, to signal that it is likely, or not likely, that the search object appears, at least partially, in the image. The edges are detected using known techniques. The edges are then converted to sequences of pixels. Edges in the search object and in the digital image are then represented as objects, in the object oriented programming sense. Each edge or segment of an edge is represented as a normalized Bezier cubic parameterized curve. The conversion from a sequence of pixels to a Bezier polynomial representation is accomplished using least squares approximation techniques. The normalization process is intended to remove the effect of size in the edge or edge segment. The original Bezier representation is also maintained for each edge, as it provides necessary location information. In the event that two edges in the search object are matched with edges in the image, their relative orientation is checked using elementary vector analysis. If the edges match and their orientation is the same, then the system signals that the object is likely to appear in the image and the coordinates in the image of the object are returned. The functioning of the algorithm is not dependent on scaling, rotation, translation, or shading of the image.

Citation Download Citation

Timothy P. Donovan and Russell E Zuck "Edge comparison in a digital image", Proc. SPIE 5817, Visual Information Processing XIV, (25 May 2005); https://doi.org/10.1117/12.602475

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KEYWORDS

Image processing

Digital imaging

Image segmentation

Edge detection

Detection and tracking algorithms

Genetic algorithms

Object oriented programming

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