Adaptive active contour model driven by fractional order fitting energy

An improved edge-based level set method combining local regional fitting information for noisy image segmentation

“…[4][5][6][7] show that the disadvantage of the constant regional coefficient is particularly serious for the images with Gaussian noise. The experimental results of our method in Figs.…”

“…Various methods have been proposed to solve this problem [1][2][3][4][5][6]. In these methods, active contour models (ACMs, also named snake or deformable models) are widely used because of their excellent properties which can provide smooth and accurate segmentation results [3][4][5][6].…”

Image Segmentation Based on Fractional Differentiation and RSF Model

“…This section focuses on testing segmentation performance of the proposed method, to ensure contrast experiment fair, we select four images, two are from ref [8] (numbered as picture I and picture II), and another two are galaxy and fingerprint pictures(numbered as picture III and picture IV), all of the four pictures are intensity inhomogeneity, and picture I and II include weak edges, picture III and picture IV involve weak texture. We take experiments using above four pictures and compare our method with LIF model, ref [13] model, RSF model. Segmentation result and segmentation time are introduced to evaluate segmentation performance of each model.…”

“…This excellent features are widely used in keeping texture details and weak edges in image processing [12] . For example, Ren et al [13] introduced fractional calculus to CV models and add fractional fitting term, Tian et al [14] employed fractional divergence operator to CV model, both methods improve capacity of weak edges location and the performance of segmenting weak edges, in addition both mentioned above methods are more robust to noise. However, both models do not work well in images with intensity inhomogeneity because they only use image local information.…”

A simple and flexible modification of Grünwald–Letnikov fractional derivative in image processing

“…For instance, fractionalorder partial differential equation-based formulation are applicable for multi-scale nonlocal contrast enhancement with texture preserving [3] and iterative learning control with high-order internal models [4]. In image processing, fractional calculus is exploited in image denoising using the diffusion equation [5][6][7][8] and in image segmentation with active contours using the fractional derivative within energy functional [9]. Mathieu et al [10] applied the fractional differentiation for edge detection.…”