Abstract
We propose a novel image segmentation algorithm using piecewise smooth (PS) approximation to image. The proposed algorithm is inspired by four well-known active contour models, i.e., Chan and Vese’ piecewise constant (PC)/smooth models, the region-scalable fitting model, and the local image fitting model. The four models share the same algorithm structure to find a PC/smooth approximation to the original image; the main difference is how to define the energy functional to be minimized and the PC/smooth function. In this article, pursuing the same idea we introduce different energy functional and PS function to search for the optimal PS approximation of the original image. The initial function with our model can be chosen as a constant function, which implies that the proposed algorithm is robust to initialization or even free of manual initialization. Experiments show that the proposed algorithm is very appropriate for a wider range of images, including images with intensity inhomogeneity and infrared ship images with low contrast and complex background.
Highlights
Image segmentation is a popular problem in image processing and computer vision, which has been studied extensively in past decades
In this study, we introduce different energy functional and piecewise smooth (PS) functions to search for the optimal PS approximation of the original image
All the spatial partial derivatives @φ=@x and @φ=@y are approximated by the central difference, and the temporal partial derivative @φ=@t is discretized as the forward difference
Summary
Image segmentation is a popular problem in image processing and computer vision, which has been studied extensively in past decades. Zhang et al [16] proposed a novel active contour model driven by local image fitting (LIF) energy.
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