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Image segmentation with adaptive region growing based on a polynomial surface model

Francis Deboeverie (UGent) , Peter Veelaert (UGent) and Wilfried Philips (UGent)
(2013) JOURNAL OF ELECTRONIC IMAGING. 22(4).
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Abstract
A new method for segmenting intensity images into smooth surface segments is presented. The main idea is to divide the image into flat, planar, convex, concave, and saddle patches that coincide as well as possible with meaningful object features in the image. Therefore, we propose an adaptive region growing algorithm based on low-degree polynomial fitting. The algorithm uses a new adaptive thresholding technique with the L∞ fitting cost as a segmentation criterion. The polynomial degree and the fitting error are automatically adapted during the region growing process. The main contribution is that the algorithm detects outliers and edges, distinguishes between strong and smooth intensity transitions and finds surface segments that are bent in a certain way. As a result, the surface segments corresponding to meaningful object features and the contours separating the surface segments coincide with real-image object edges. Moreover, the curvature-based surface shape information facilitates many tasks in image analysis, such as object recognition performed on the polynomial representation. The polynomial representation provides good image approximation while preserving all the necessary details of the objects in the reconstructed images. The method outperforms existing techniques when segmenting images of objects with diffuse reflecting surfaces.
Keywords
MEAN SHIFT, APPROXIMATION, FEATURE-SPACE, NORMALIZED CUTS, UNSUPERVISED SEGMENTATION, COMPRESSION, INFORMATION, EXTRACTION, MESH SEGMENTATION, ALGORITHM

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MLA
Deboeverie, Francis, et al. “Image Segmentation with Adaptive Region Growing Based on a Polynomial Surface Model.” JOURNAL OF ELECTRONIC IMAGING, vol. 22, no. 4, 2013, doi:10.1117/1.JEI.22.4.043004.
APA
Deboeverie, F., Veelaert, P., & Philips, W. (2013). Image segmentation with adaptive region growing based on a polynomial surface model. JOURNAL OF ELECTRONIC IMAGING, 22(4). https://doi.org/10.1117/1.JEI.22.4.043004
Chicago author-date
Deboeverie, Francis, Peter Veelaert, and Wilfried Philips. 2013. “Image Segmentation with Adaptive Region Growing Based on a Polynomial Surface Model.” JOURNAL OF ELECTRONIC IMAGING 22 (4). https://doi.org/10.1117/1.JEI.22.4.043004.
Chicago author-date (all authors)
Deboeverie, Francis, Peter Veelaert, and Wilfried Philips. 2013. “Image Segmentation with Adaptive Region Growing Based on a Polynomial Surface Model.” JOURNAL OF ELECTRONIC IMAGING 22 (4). doi:10.1117/1.JEI.22.4.043004.
Vancouver
1.
Deboeverie F, Veelaert P, Philips W. Image segmentation with adaptive region growing based on a polynomial surface model. JOURNAL OF ELECTRONIC IMAGING. 2013;22(4).
IEEE
[1]
F. Deboeverie, P. Veelaert, and W. Philips, “Image segmentation with adaptive region growing based on a polynomial surface model,” JOURNAL OF ELECTRONIC IMAGING, vol. 22, no. 4, 2013.
@article{4149797,
  abstract     = {{A new method for segmenting intensity images into smooth surface segments is presented. The main idea is to divide the image into flat, planar, convex, concave, and saddle patches that coincide as well as possible with meaningful object features in the image. Therefore, we propose an adaptive region growing algorithm based on low-degree polynomial fitting. The algorithm uses a new adaptive thresholding technique with the L∞ fitting cost as a segmentation criterion. The polynomial degree and the fitting error are automatically adapted during the region growing process. The main contribution is that the algorithm detects outliers and edges, distinguishes between strong and smooth intensity transitions and finds surface segments that are bent in a certain way. As a result, the surface segments corresponding to meaningful object features and the contours separating the surface segments coincide with real-image object edges. Moreover, the curvature-based surface shape information facilitates many tasks in image analysis, such as object recognition performed on the polynomial representation. The polynomial representation provides good image approximation while preserving all the necessary details of the objects in the reconstructed images. The method outperforms existing techniques when segmenting images of objects with diffuse reflecting surfaces.}},
  articleno    = {{043004}},
  author       = {{Deboeverie, Francis and Veelaert, Peter and Philips, Wilfried}},
  issn         = {{1017-9909}},
  journal      = {{JOURNAL OF ELECTRONIC IMAGING}},
  keywords     = {{MEAN SHIFT,APPROXIMATION,FEATURE-SPACE,NORMALIZED CUTS,UNSUPERVISED SEGMENTATION,COMPRESSION,INFORMATION,EXTRACTION,MESH SEGMENTATION,ALGORITHM}},
  language     = {{eng}},
  number       = {{4}},
  pages        = {{13}},
  title        = {{Image segmentation with adaptive region growing based on a polynomial surface model}},
  url          = {{http://doi.org/10.1117/1.JEI.22.4.043004}},
  volume       = {{22}},
  year         = {{2013}},
}
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