Abstract
Image segmentation is a process that densely classifies image pixels into different regions corresponding to real world objects. However, this correspondence is not always exact in images since there are many uncertainty factors, e.g., recognition hesitation, imaging equipment, condition, and atmosphere environment. To achieve the segmentation result with low uncertainty and reduce the influence on the subsequent procedures, e.g., image parsing and image understanding, we propose a novel stochastic active contour model based on intuitionistic fuzzy set, in which the hesitation degree is leveraged to model the recognition uncertainty in image segmentation. The advantages of our model are as follows. (1) Supported by fuzzy partition, our model is robust against image noise and inhomogeneity. (2) Benefiting from the stochastic process, our model easily crosses saddle points of energy functional. (3) Our model realizes image segmentation with low uncertainty and co-produces the quantitative uncertainty degree to the segmentation results, which is helpful to improve reliability of intelligent image systems. The associated experiments suggested that our model could obtain competitive segmentation results compared to the relevant state-of-the-art active contour models and could provide segmentation with a pixel-wise uncertainty degree.
Highlights
Image segmentation is an important technology in computer vision, and it has been widely applied to many fields [1,2,3]
To avoid the aforementioned accidents, we model the uncertainty in segmentation via intuitionistic fuzzy set and design a novel energy functional for stochastic active contour model (ACM) to realize a segmentation with low uncertainty
We ran our method on 10 different images randomly selected from BSD-500 [41], generated multiple stochastic images of each selected image, and calculated the variance between the segmentation result based on the stochastic image sequence
Summary
Image segmentation is an important technology in computer vision, and it has been widely applied to many fields [1,2,3]. LSM inspired by geodesics active contour model [6] to avoid boundary leakage caused by the stopper function based on image gradient This method, is sensitive to initial curves. Mumford and Shah [7] proposed a smooth image approximation for segmentation; its computation cost is pretty high Simplifying this model by a piecewise constant function for the image approximation, Chan and Vese proposed a region-based LSM, called CV model [8], to extract objects without clear boundaries, but it could not obtain the desired result on inhomogeneous images. Considering the great progress of ACM in recent years, we leverage IFS to model this uncertainty, and firstly introduce it into stochastic ACM by designing a new energy functional By optimizing this functional, we realize image segmentation with low uncertainty.
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