Abstract
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.
Highlights
Image segmentation, which is the extraction of an object from the background in an image, is one of the essential techniques in areas of image processing and computer vision [1, 2]
2.2 Intuitionistic fuzzy sets and intuitionistic fuzzy entropy we present the basic elements of intuitionistic fuzzy set theory and two fuzzy membership functions, which will be used in the wave transformation model and image segmentation
5 Conclusions In this paper, we presented a novel algorithm for the segmentation of uneven background lighting images with strong noise injection
Summary
Image segmentation, which is the extraction of an object from the background in an image, is one of the essential techniques in areas of image processing and computer vision [1, 2]. Block-based methods are another category of local thresholding algorithms, which divide the image into different sub-blocks. A wave transformation model, which is introduced by Wei et al, is a prospective idea for uneven lighting image segmentation [36] They consider an image surface as a three-dimensional terrain that is composed of mountains and valleys, corresponding to peaks and troughs, respectively, and partition the sub-regions with the local peaks and troughs in multi-directions. The vectors are compressed to one dimension using the principal component analysis (PCA) method, and an Otsu global method is employed to find an optimal wave threshold for segmenting the matrix This algorithm does not require image partitioning and can yield good segmentation results for uneven light images.
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