Bayesian models for finding and grouping junctions

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Title: Bayesian models for finding and grouping junctions
Authors: Cazorla, Miguel | Escolano, Francisco | Gallardo López, Domingo | Rizo, Ramón
Research Group/s: Robótica y Visión Tridimensional (RoViT) | Laboratorio de Investigación en Visión Móvil (MVRLab) | Informática Industrial e Inteligencia Artificial
Center, Department or Service: Universidad de Alicante. Departamento de Ciencia de la Computación e Inteligencia Artificial
Keywords: Bayesian methods | Junction detection | Junction grouping
Knowledge Area: Ciencia de la Computación e Inteligencia Artificial
Issue Date: 1999
Publisher: Springer Berlin / Heidelberg
Citation: CAZORLA, M.A., et al. "Bayesian models for finding and grouping junctions". En: Energy Minimization Methods in Computer Vision and Pattern Recognition : Second International Workshop, EMMCVPR’99 York, UK, July 26–29, 1999 Proceedings / Edwin R. Hancock, Marcello Pelillo (Eds.). Berlin : Springer, 1999. (Lecture Notes in Computer Science; 1654). ISBN 3-540-66294-4, pp. 70-82
Abstract: In this paper, we propose two Bayesian methods for detecting and grouping junctions. Our junction detection method evolves from the Kona approach, and it is based on a competitive greedy procedure inspired in the region competition method. Then, junction grouping is accomplished by finding connecting paths between pairs of junctions. Path searching is performed by applying a Bayesian A* algorithm that has been recently proposed. Both methods are efficient and robust, and they are tested with synthetic and real images.
ISBN: 3-540-66294-4
ISSN: 0302-9743 (Print) | 1611-3349 (Online)
DOI: 10.1007/3-540-48432-9_6
Language: eng
Type: info:eu-repo/semantics/conferenceObject
Rights: The original publication is available at
Peer Review: si
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Appears in Collections:INV - RoViT - Comunicaciones a Congresos, Conferencias, etc.
INV - i3a - Comunicaciones a Congresos, Conferencias, etc.

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