A novel segmentation method for uneven lighting image with noise injection based on non-local spatial information and intuitionistic fuzzy entropy

Local thresholding methods for uneven lighting image segmentation always have the limitations that they are very sensitive to noise injection and that the performance relies largely upon the choice of the initial window size. This paper proposes a novel algorithm for segmenting uneven lighting image...

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Bibliographic Details
Published in:EURASIP journal on advances in signal processing 2017-10, Vol.2017 (1), p.1-22, Article 74
Main Authors: Yu, Haiyan, Fan, Jiulun
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Engineering
Entropy (Information theory)
Illumination
Image processing
Image segmentation
Intuitionistic fuzzy theory
Light
Lighting
Local thresholding
Methods
Noise
Noise sensitivity
Non local spatial constraint
Pixels
Quantum Information Technology
Signal,Image and Speech Processing
Spatial data
Spintronics
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Summary:Local thresholding methods for uneven lighting image segmentation always have the limitations that they are very sensitive to noise injection and that the performance relies largely upon the choice of the initial window size. This paper proposes a novel algorithm for segmenting uneven lighting images with strong noise injection based on non-local spatial information and intuitionistic fuzzy theory. We regard an image as a gray wave in three-dimensional space, which is composed of many peaks and troughs, and these peaks and troughs can divide the image into many local sub-regions in different directions. Our algorithm computes the relative characteristic of each pixel located in the corresponding sub-region based on fuzzy membership function and uses it to replace its absolute characteristic (its gray level) to reduce the influence of uneven light on image segmentation. At the same time, the non-local adaptive spatial constraints of pixels are introduced to avoid noise interference with the search of local sub-regions and the computation of local characteristics. Moreover, edge information is also taken into account to avoid false peak and trough labeling. Finally, a global method based on intuitionistic fuzzy entropy is employed on the wave transformation image to obtain the segmented result. Experiments on several test images show that the proposed method has excellent capability of decreasing the influence of uneven illumination on images and noise injection and behaves more robustly than several classical global and local thresholding methods.
ISSN:1687-6180
1687-6172
1687-6180
DOI:10.1186/s13634-017-0509-5