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Recognizing solid objects by alignment with an image

Author: Daniel Peter Huttenlocher; Shimon Ullman
Publisher: Ithaca, N.Y. : Cornell University, Dept. of Computer Science, 1989.
Series: Cornell University.; Department of Computer Science.; Technical report
Edition/Format:   Book : EnglishView all editions and formats
Database:WorldCat
Summary:
Abstract: "In this paper we consider the problem of recognizing solid objects from a single two-dimensional image of a three-dimensional scene. We develop a new method for computing a transformation from a three-dimensional model coordinate frame to the two-dimensional image coordinate frame, using three pairs of model and image points. We show that this transformation always exists for three non-colinear points,
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Document Type: Book
All Authors / Contributors: Daniel Peter Huttenlocher; Shimon Ullman
OCLC Number: 21501980
Notes: "March 1989."
Description: 34 pages : illustrations ; 28 cm.
Series Title: Cornell University.; Department of Computer Science.; Technical report
Responsibility: Daniel P. Huttenlocher, Shimon Ullman.

Abstract:

Abstract: "In this paper we consider the problem of recognizing solid objects from a single two-dimensional image of a three-dimensional scene. We develop a new method for computing a transformation from a three-dimensional model coordinate frame to the two-dimensional image coordinate frame, using three pairs of model and image points. We show that this transformation always exists for three non-colinear points, and is unique up to a reflective ambiguity. The solution method is closed-form and only involves second order equations.

We have implemented a recognition system that uses this transformation method to determine possible alignments of a model with an image. Each of these hypothesized matches is verified by comparing the entire edge contours of the aligned object with the image edges. Using the entire edge contours for verification, rather than a few local feature points, reduces the chance of finding false matches. The system has been tested on partly occluded objects in highly cluttered scenes."

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