Describing Reflectances for Color Segmentation Robust to Shadows, Highlights, and Textures

The segmentation of a single material reflectance is a challenging problem due to the considerable variation in image measurements caused by the geometry of the object, shadows, and specularities. The combination of these effects has been modeled by the dichromatic reflection model. However, the app...

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Bibliographic Details
Published in:IEEE transactions on pattern analysis and machine intelligence 2011-05, Vol.33 (5), p.917-930
Main Authors: Vazquez, E, Baldrich, R, van de Weijer, J, Vanrell, M
Format: Article
Language:English
Subjects:
Applied sciences
Artificial intelligence
color
Computer science
control theory
systems
Exact sciences and technology
Histograms
Image color analysis
Image contrast
Image segmentation
Materials
Mathematical models
Pattern recognition. Digital image processing. Computational geometry
Reflectance
Robustness
Segmentation
Shadows
Shape
Spatial coherence
Studies
Surface layer
Texture
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Summary:The segmentation of a single material reflectance is a challenging problem due to the considerable variation in image measurements caused by the geometry of the object, shadows, and specularities. The combination of these effects has been modeled by the dichromatic reflection model. However, the application of the model to real-world images is limited due to unknown acquisition parameters and compression artifacts. In this paper, we present a robust model for the shape of a single material reflectance in histogram space. The method is based on a multilocal creaseness analysis of the histogram which results in a set of ridges representing the material reflectances. The segmentation method derived from these ridges is robust to both shadow, shading and specularities, and texture in real-world images. We further complete the method by incorporating prior knowledge from image statistics, and incorporate spatial coherence by using multiscale color contrast information. Results obtained show that our method clearly outperforms state-of-the-art segmentation methods on a widely used segmentation benchmark, having as a main characteristic its excellent performance in the presence of shadows and highlights at low computational cost.
ISSN:0162-8828
1939-3539
DOI:10.1109/TPAMI.2010.146