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Conference proceeding
Texture Segmentation by Contractive Decomposition and Planar Grouping
GRAPH-BASED REPRESENTATIONS IN PATTERN RECOGNITION, PROCEEDINGS, v 5534, pp 343-352
01 Jan 2009
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Abstract
Image segmentation has long been an important problem in the computer vision community. In our recent work we have addressed the problem of texture segmentation, where we combined top-down and bottom-up views of the image into a unified procedure. In this paper we extend our work by proposing a modified procedure which makes use of graphs of image regions. In the top-down procedure a quadtree of image region descriptors is obtained ill which a novel affine contractive transformation based on neighboring regions is used to update descriptors and determine stable segments. In the bottom-up procedure we form a planar graph on the resulting stable segments, where edges are present between vertices representing neighboring image regions. We then use a vertex merging technique to obtain the final segmentation. We verify the effectiveness of this procedure by demonstrating results which compare well to other recent techniques.
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Details
- Title
- Texture Segmentation by Contractive Decomposition and Planar Grouping
- Creators
- Anders Bjorholm Dahl - Technical University of DenmarkPeter Bogunovich - Drexel UniversityAli Shokoufandeh - Drexel University
- Contributors
- A Torsello (Editor)F Escolano (Editor)L Brun (Editor)
- Publication Details
- GRAPH-BASED REPRESENTATIONS IN PATTERN RECOGNITION, PROCEEDINGS, v 5534, pp 343-352
- Series
- Lecture Notes in Computer Science
- Publisher
- Springer Nature
- Number of pages
- 3
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Computer Science
- Web of Science ID
- WOS:000268444800035
- Scopus ID
- 2-s2.0-70349842985
- Other Identifier
- 991019170378704721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Theory & Methods
- Imaging Science & Photographic Technology