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Adaptive Pixel-Based Data Fusion for Boundary Detection

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Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR 1999)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1654))

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Abstract

It is of practical importance to fuse data obtained by multiple sensors for improving the performance of computer vision systems. This paper introduces an algorithm for pixel-based data fusion on the variational framework. An adaptive system fuses data effectively using a variational technique. Previously, we have introduced a technique to fuse gray-scale image and texture extracting features for segmenting an image with both textured and non-textured surfaces. This paper extends the study for more general multi-valued data and improve the previous algorithm in terms of the performance and speed.

Research partially supported by ONR Grant N00014-97-1-1163 and ARO Grant DAAH04-96-10326

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Author information

Authors and Affiliations

  1. Intelligent Systems Laboratory, Department of Computer Science, University of South Carolina, Columbia, SC, 29208, USA

    Toshiro Kubota

Authors
  1. Toshiro Kubota
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of York, York, Y01 5DD, UK

    Edwin R. Hancock

  2. Dipartimento di Informatica, Università Ca’ Foscari di Venezia, Via Torino 155, I-30172, Venezia Mestre, Italy

    Marcello Pelillo

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© 1999 Springer-Verlag Berlin Heidelberg

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Kubota, T. (1999). Adaptive Pixel-Based Data Fusion for Boundary Detection. In: Hancock, E.R., Pelillo, M. (eds) Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 1999. Lecture Notes in Computer Science, vol 1654. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48432-9_13

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  • DOI: https://doi.org/10.1007/3-540-48432-9_13

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-66294-5

  • Online ISBN: 978-3-540-48432-5

  • eBook Packages: Springer Book Archive

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