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Linear colour segmentation revisited
Author(s): Anna Smagina; Valentina Bozhkova; Sergey Gladilin; Dmitry Nikolaev
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Paper Abstract

In this work we discuss the known algorithms for linear colour segmentation based on a physical approach and propose a new modification of segmentation algorithm. This algorithm is based on a region adjacency graph framework without a pre-segmentation stage. Proposed edge weight functions are defined from linear image model with normal noise. The colour space projective transform is introduced as a novel pre-processing technique for better handling of shadow and highlight areas. The resulting algorithm is tested on a benchmark dataset consisting of the images of 19 natural scenes selected from the Barnard’s DXC-930 SFU dataset and 12 natural scene images newly published for common use. The dataset is provided with pixel-by-pixel ground truth colour segmentation for every image. Using this dataset, we show that the proposed algorithm modifications lead to qualitative advantages over other model-based segmentation algorithms, and also show the positive effect of each proposed modification. The source code and datasets for this work are available for free access at http://github.com/visillect/segmentation.

Paper Details

Date Published: 15 March 2019
PDF: 13 pages
Proc. SPIE 11041, Eleventh International Conference on Machine Vision (ICMV 2018), 110410F (15 March 2019); doi: 10.1117/12.2523007
Show Author Affiliations
Anna Smagina, Institute for Information Transmission Problems (Russian Federation)
Valentina Bozhkova, Institute for Information Transmission Problems (Russian Federation)
Sergey Gladilin, Institute for Information Transmission Problems (Russian Federation)
Dmitry Nikolaev, Institute for Information Transmission Problems (Russian Federation)


Published in SPIE Proceedings Vol. 11041:
Eleventh International Conference on Machine Vision (ICMV 2018)
Antanas Verikas; Dmitry P. Nikolaev; Petia Radeva; Jianhong Zhou, Editor(s)

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