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Approximative Coding Methods for Channel Representations
Visionists AB, Gothenburg, Sweden.
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-6096-3648
2017 (English)In: Journal of Mathematical Imaging and Vision, ISSN 0924-9907, E-ISSN 1573-7683, Vol. 68, no 2Article in journal (Refereed) Published
Abstract [en]

Most methods that address computer vision prob-lems require powerful visual features. Many successfulapproaches apply techniques motivated from nonparametricstatistics. The channel representation provides a frameworkfornonparametricdistributionrepresentation.Althoughearlywork has focused on a signal processing view of the rep-resentation, the channel representation can be interpretedin probabilistic terms, e.g., representing the distribution oflocal image orientation. In this paper, a variety of approxi-mative channel-based algorithms for probabilistic problemsare presented: a novel efficient algorithm for density recon-struction, a novel and efficient scheme for nonlinear griddingof densities, and finally a novel method for estimating Copuladensities. The experimental results provide evidence that byrelaxing the requirements for exact solutions, efficient algo-rithms are obtained

Place, publisher, year, edition, pages
Springer, 2017. Vol. 68, no 2
Keyword [en]
Visual features, Channel representations, Approximative density estimation. Maximum entropy
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:liu:diva-143208DOI: 10.1007/s10851-017-0775-8OAI: oai:DiVA.org:liu-143208DiVA, id: diva2:1159593
Available from: 2017-11-23 Created: 2017-11-23 Last updated: 2017-11-23

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CiteExportLink to record
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Citation style
  • apa
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  • de-DE
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