Vertices and corners: normalized average detection

Raashid Malik; Hui Ren

doi:10.1117/12.150177

20 August 1993 Vertices and corners: normalized average detection

Raashid Malik, Hui Ren

Proceedings Volume 2055, Intelligent Robots and Computer Vision XII: Algorithms and Techniques; (1993) https://doi.org/10.1117/12.150177
Event: Optical Tools for Manufacturing and Advanced Automation, 1993, Boston, MA, United States

Abstract

Most of the information regarding the shape of polyhedral objects is preserved in the edges and the vertices of these objects. Gray level images of scenes containing such objects are often processed to extract edge and vertex information to produce equivalent line sketches. An accurate line sketch of a scene serves as an effective input to high level vision systems concerned with scene understanding or object recognition. The performance of these systems is therefore greatly dependent on the accuracy of the line sketch. The work reported in this paper addresses the issues associated with generating accurate line sketches from gray level images. The methods described here have been implemented and tested with real and synthetic images and are compared to other vertex or corner detection techniques. The performance of the vertex detector is assessed using simulation runs on images with varied signal-to-noise ratios. The computational performance of this algorithm is evaluated and assessed by operating directly on the gray-scale image.

Citation Download Citation

Raashid Malik and Hui Ren "Vertices and corners: normalized average detection", Proc. SPIE 2055, Intelligent Robots and Computer Vision XII: Algorithms and Techniques, (20 August 1993); https://doi.org/10.1117/12.150177

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