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Proceedings Paper

Image segmentation using Gaussian curvature
Author(s): Neelima Shrikhande; Sripriya Ramaswamy
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Paper Abstract

One of the central problems of computer vision is segmentation of images into salient features such as edges and surfaces. Different kinds of similarity criteria can be used to group related pixels together. One such criterion is the curvature of surfaces in an image of a multiobject scene that contains several objects with different shapes. In practice, however, curvature is difficult to calculate because small amount of noise can cause large amounts of errors in calculations of first and second derivatives. In this paper, we use a discrete approximation of Gaussian curvature that is efficient to compute. The approximation is used to segment the image into individual surfaces. Both synthetic and real images have been tested. Results appear quite encouraging.

Paper Details

Date Published: 3 October 1995
PDF: 8 pages
Proc. SPIE 2588, Intelligent Robots and Computer Vision XIV: Algorithms, Techniques, Active Vision, and Materials Handling, (3 October 1995); doi: 10.1117/12.222719
Show Author Affiliations
Neelima Shrikhande, Central Michigan Univ. (United States)
Sripriya Ramaswamy, Central Michigan Univ. (United States)


Published in SPIE Proceedings Vol. 2588:
Intelligent Robots and Computer Vision XIV: Algorithms, Techniques, Active Vision, and Materials Handling
David P. Casasent, Editor(s)

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