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Recently, image segmentation based on graph cut methods has shown remarkable performance on a set of image data. Although the kernel graph cut method provides good performance, its performance is highly dependent on the data mapping to the transformation space and image features. The entropy-based kernel graph cut method is suitable for segmentation of textured images. Nonetheless, its segmentation quality remains significantly contingent on the accuracy and richness of feature space representation and kernel centers. This paper introduces an entropy-based kernel graph cut method, which leverages the discriminative feature space extracted from SqueezeNet, a deep neural network. The fusion of SqueezeNet’s features enriches the segmentation process by capturing high-level semantic information. Moreover, the extraction of kernel centers is refined through a weighted k-means approach, contributing further to the segmentation’s precision and effectiveness. The proposed method, while exploiting the benefits of suitable computational load of graph cut methods, will be a suitable alternative for segmenting textured images. Laboratory results have been taken on a set of well-known datasets that include textured shapes in order to evaluate the efficiency of the algorithm compared to other well-known methods in the field of kernel graph cut. [ABSTRACT FROM AUTHOR]

Image restoration has been a longstanding challenge in image processing, involving the enhancement of image content to extract valuable information. Nonlinear models have demonstrated their efficacy in eliminating additive noise, which motivates our goal of investigating a novel nonlocal nonlinear reaction-diffusion model for noise removal. The proposed model integrates a fractional diffusion equation with a nonlinear nonlocal p -Laplace operator, with the fidelity term employing a weak norm to better capture oscillatory patterns and intricate details in textured images. Using the Schauder fixed point theorem, the well-posedness of the proposed model solution is established. The experimental results confirm the effectiveness and efficiency of the proposed model, providing validation for its practical utility. [ABSTRACT FROM AUTHOR]

This paper addresses the analysis of textured images using the symmetric positive semi-definite matrix. In particular, a field of symmetric positive semi-definite matrices is used to estimate the structural information represented by the local orientation and the degree of anisotropy in structured and sinusoid-like textured images. In order to ensure faithful local structure estimation, an adaptive algorithm for the regularization of the extent of gradient fields smoothing is proposed. Results obtained on different texture samples show the strength of the proposed method in accurately representing the local variation of orientations in the underlying textured images, which paves the way towards an accurate analysis of the texture structures.

In this paper, we focus on the mathematical and numerical study of a variable exponent nonlocal p ( X ) -Laplacian equation for image denoising. Based on the Semigroup Theory, we prove the existence and uniqueness of solution for the proposed model. To illustrate the efficiency and effectiveness of our model, we provide the denoising experimental results as well we compare it with some existing models in the literature. [ABSTRACT FROM AUTHOR]

Highlights: [•] We present an objective performance analysis of statistical edge detection. [•] A controlled dataset of images and tests is given and fully analysed. [•] We show how statistical tests can outperform traditional edge detection methods. [•] We present recommendations for the tests, giving guidelines for their use. [•] Non-parametric tests perform best overall, giving improvements over all tests. [Copyright &y& Elsevier]

Abstract: This paper presents a new unsupervised hybrid classifier that combines several base classifiers through a fuzzy multicriteria decision making (MCDM) approach. The base classifiers are: fuzzy clustering, parametric and non-parametric Bayesian approaches, self-organizing feature maps and two versions of learning vector quantization. During the learning phase different partitions are established until a valid partition is found. The partitioning and validation are two automatic processes based on validation measurements. These measures allow computing the competences of each base classifier which are mapped as the weights to be used during the decision process through the MCDM. The design of the unsupervised classifier from supervised base classifiers and the automatic computation of the competences make the main contributions of this paper. Although the method is designed for six classifiers it can be extended for a greater number of classifiers. The method is applied for classifying textures in natural images. The analysis of the results shows that the performance of the proposed method is superior to other hybrid methods and the single usage of existing classification methods. [Copyright &y& Elsevier]