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Article
Self-Calibration of a Moving Camera from Point Correspondences and Fundamental Matrices
We address the problem of estimating three-dimensional motion, and structure from motion with an uncalibrated moving camera. We show that point correspondences between three images, and the fundamental matrice...
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Chapter and Conference Paper
A stability analysis of the Fundamental matrix
The Fundamental matrix is a key concept when working with uncalibrated images and multiple viewpoints. It contains all the available geometric information and enables to recover the epipolar geometry from uncalib...
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Chapter and Conference Paper
Camera self-calibration: Theory and experiments
The problem of finding the internal orientation of a camera (camera calibration) is extremely important for practical applications. In this paper a complete method for calibrating a camera is presented. In con...
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Chapter
The Representation, Recognition, and Positioning of 3-D Shapes from Range Data
The task of recognizing and positioning rigid objects in 3-D space is important for robotics and navigation applications. In this paper we analyze the task requirements in terms of what information needs to be...
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Chapter and Conference Paper
Object Representation, Identification and Positioning from Range Data
We review the types of representations (both single level and hierarchical) and the matching algorithms which have been found useful in the design and implementation of a 3-D vision system that can model, iden...
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Chapter
Towards a Flexible Vision System
A Vision System designed for building accurate models of industrial parts is described. Potential applications include tolerancing testing, data base acquisition and automatic recognition of objects. The syste...
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Chapter and Conference Paper
Stochastic Labeling Techniques for Recognition of Partially Visible 2-D and 3-D Objects
In this paper we show how Stochastic labeling techniques can be used efficiently to recognize partially visible objects in two and three dimensions.
