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Segmentation of range images as the search for geometric parametric models

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Abstract

Segmentation of range images has long been considered in computer vision as an important but extremely difficult problem. In this paper we present a new paradigm for the segmentation of range images into piecewise continuous surfaces. Data aggregation is performed via model recovery in terms of variable-order bi-variate polynomials using iterative regression. Model recovery is initiated independently in regularly placed seed regions in the image. All the recovered models are potential candidates for the final description of the data. Selection of the models is defined as a quadratic Boolean problem, and the solution is sought by the WTA (winner-takes-all) technique, which turns out to be a good compromise between the speed of computation and the accuracy of the solution. The overall efficiency of the method is achieved by combining model recovery and model selection in an iterative way. Partial recovery of the models is followed by the selection (optimization) procedure and only the “best” models are allowed to develop further.

The major novelty of the approach lies in an effective combination of simple component algorithms, which stands in contrast to methods which attempt to solve the problem in a single processing step using sophisticated means. We present the results on several real range images.

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Author notes

  1. Aleš Leonardis

    Present address: Department of Electrical Engineering and Computer Science, Computer Vision Laboratory, University of Ljubljana, Tržaška c. 25, 61001, Ljubljana, Slovenia

  2. Alok Gupta

    Present address: Siemens Corporate Research at Princeton, New Jersey, USA

Authors and Affiliations

  1. GRASP Laboratory, Department of Computer and Information Science, University of Pennsylvania, 19104, Philadelphia, PA

    Aleš Leonardis, Alok Gupta & Ruzena Bajcsy

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  1. Aleš Leonardis
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  2. Alok Gupta
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  3. Ruzena Bajcsy
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Leonardis, A., Gupta, A. & Bajcsy, R. Segmentation of range images as the search for geometric parametric models. Int J Comput Vision 14, 253–277 (1995). https://doi.org/10.1007/BF01679685

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