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Algorithms for simple object reconstruction using the largest possible object approach*

Published online by Cambridge University Press:  09 March 2009

Edward T. Lee  and
Fred Y. Wu
Edward T. Lee
Affiliation:
Department of Electrical and Computer Engineering, Florida International University, University Park Campus, Miami, Florida 33199 (USA).
Fred Y. Wu
Affiliation:
Department of Electrical and Computer Engineering, University of Miami, P.O. Box 248294, Coral Gables, Florida 33124 (USA).
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Summary

Recently, three-dimensional motion analysis and shape recovery have attracted growing attention as promising avenues of approach to image understanding, object reconstruction as well as computer vision for robotic Systems. The image generation problem and the model generation problem are presented. More specifically, the inputs to the image generator are an old image, object model, motion specification, and hidden line and hidden surface algorithms. The output is a new image. Since the object model is given, the top-down approach is usually used. On the other hand, for the model generation problem, the input is an image sequence while the output is an object model. Since the object model is not given, and bottom-up approach is usually used.

In this paper, the largest possible object approach is proposed and the advantages of this approach are stated. They are:

1. This approach may be applicable to objects with planar surfaces as well as nonplanar surfaces.

2. This approach may be applicable to the case that there are more than one face change per frame.

3. This approach may be applicable when the camera is moving.

4. This approach may be applicable when the object is measured by several measuring stations.

By using this approach, algorithms for simple object reconstruction given a sequence of pictures are presented together with illustrative examples. The relevance and importance of this work are discussed.

The results of this paper may have useful applications in object reconstruction, pictorial data reduction and computer vision for robotic Systems.

Type
Article
Information
Robotica , Volume 10 , Issue 4 , July 1992 , pp. 377 - 381
Copyright
Copyright © Cambridge University Press 1992

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