Review of Level Set in Image Segmentation

Wang, Zhaobin; Ma, Baozhen; Zhu, Ying

doi:10.1007/s11831-020-09463-9

Cited by 22 publications

(17 citation statements)

References 91 publications

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“…Data processing is the core of the module, including text classification, privacy processing, classification thesaurus comparison, and other functions. The nodes and rules of module node management module provide judgment basis for text classification, and the module lays a solid foundation for the realization of text classification function [19]. The algorithm design of text classification is as follows: starting from the root node, the data is matched with the rules of the node; if the data matches the rules, the recursion goes down layer by layer; if the data does not match the rules, the recursion of its subordinate nodes is stopped; finally, the data is allocated to the node of the deepest level that meets the rules.…”

Section: Image Segmentation Algorithm Text Classificationmentioning

confidence: 99%

Application of Image Segmentation Algorithm Based on Partial Differential Equation in Legal Case Text Classification

Sun

2021

Advances in Mathematical Physics

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As a means of regulating people’s code of conduct, law has a close relationship with text, and text data has been growing exponentially. Managing and classifying huge text data have become a huge challenge. The PDES image segmentation algorithm is an effective natural language processing method for text classification management. Based on the study of image segmentation algorithm and legal case text classification theory, an image segmentation model based on partial differential equation is proposed, in which diffusion indirectly acts on level set function through auxiliary function. The software architecture of image segmentation algorithm text classification system is proposed by using computer technology and three-layer architecture model, which can improve the classification ability of text classification algorithm. The validity of pDE image segmentation model is verified by experiments. The experimental results show that the model completes the legal case text classification, the performance of each functional module of the legal case text classification system is good, and the efficiency and quality of the legal case text classification are improved.

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Section: Image Segmentation Algorithm Text Classificationmentioning

confidence: 99%

Application of Image Segmentation Algorithm Based on Partial Differential Equation in Legal Case Text Classification

Sun

2021

Advances in Mathematical Physics

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“…As one of the region-based models, the Mumford-Shah model is the basis of variational image segmentation, and its basic idea is to segment images with segmented smooth images and their boundary approximations; however, the problem is unsolvable due to the inconsistent dimensionality of images and contour lines [18]. To facilitate direct computation, the literature introduced a level set function to divide the region, expressed the segmentation line length in terms of total variation (TV), and transformed the Mumford-Shah model of segmented constant-value degeneracy into the Chan-Vese variational model of integration over the region, and Huang et al directly adopted the binary labeling function and proposed the Chan-Vese variational model based on the binary labeling function [19].…”

Section: Current Status Of Researchmentioning

confidence: 99%

Feature Extraction and Recognition of Medical CT Images Based on Mumford-Shah Model

Shen

Zuo

2021

Advances in Mathematical Physics

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In this paper, we propose an improved algorithm based on the active contour model Mumford-Shah model for CT images, which is the subject of this study. After analyzing the classical Mumford-Shah model and related improvement algorithms, we found that most of the improvement algorithms start from the initialization strategy of the model and the minimum value solution of the energy generalization function, so we will also improve the classical Mumford-Shah model from these two perspectives. For the initialization strategy of the Mumford-Shah model, we propose to first reduce the dimensionality of the image data by the PCA principal component analysis method, and for the reduced image feature vector, we use K -means, a general clustering method, as the initial position algorithm of the segmentation curve. For the image data that have completed the above two preprocessing processes, we then use the Mumford-Shah model for image segmentation. The Mumford-Shah curve evolution model solves the image segmentation by finding the minimum of the energy generalization of its model to obtain the optimal result of image segmentation, so for solving the minimum of the Mumford-Shah model, we first optimize the discrete problem of the energy generalization of the model by the convex relaxation technique and then use the Chambolle-Pock pairwise algorithm We then use the Chambolle-Pock dual algorithm to solve the optimization problem of the model after convex relaxation and finally obtain the image segmentation results. Finally, a comparison with the existing model through many numerical experiments shows that the model proposed in this paper calculates the texture image segmentation with high accuracy and good edge retention. Although the work in this paper is aimed at two-phase image segmentation, it can be easily extended to multiphase segmentation problems.

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“…Numbers of articles were dedicated to the segmentation of color images. Many of them were using k -means [ 46 ], Gaussian mixture based models (GMM) [ 47 ], graph-based approaches [ 48 , 49 ], neural networks [ 50 , 51 , 52 , 53 ], support vector machines (SVM) [ 54 , 55 , 56 ], shape index [ 57 ], level set models [ 58 , 59 ] etc. A significant amount of research has been devoted to rust images.…”

Section: Introductionmentioning

confidence: 99%

Multiclass Level-Set Segmentation of Rust and Coating Damages in Images of Metal Structures

Bembenek

Mandziy

Ivasenko

et al. 2022

Sensors

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This paper describes the combined detection of coating and rust damages on painted metal structures through the multiclass image segmentation technique. Our prior works were focused solely on the localization of rust damages and rust segmentation under different ambient conditions (different lighting conditions, presence of shadows, low background/object color contrast). This paper method proposes three types of damages: coating crack, coating flaking, and rust damage. Background, paint flaking, and rust damage are objects that can be separated in RGB color-space alone. For their preliminary classification SVM is used. As for paint cracks, color features are insufficient for separating it from other defect types as they overlap with the other three classes in RGB color space. For preliminary paint crack segmentation we use the valley detection approach, which analyses the shape of defects. A multiclass level-set approach with a developed penalty term is used as a framework for the advanced final damage segmentation stage. Model training and accuracy assessment are fulfilled on the created dataset, which contains input images of corresponding defects with respective ground truth data provided by the expert. A quantitative analysis of the accuracy of the proposed approach is provided. The efficiency of the approach is demonstrated on authentic images of coated surfaces.

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Review of Level Set in Image Segmentation

Cited by 22 publications

References 91 publications

Application of Image Segmentation Algorithm Based on Partial Differential Equation in Legal Case Text Classification

Application of Image Segmentation Algorithm Based on Partial Differential Equation in Legal Case Text Classification

Feature Extraction and Recognition of Medical CT Images Based on Mumford-Shah Model

Multiclass Level-Set Segmentation of Rust and Coating Damages in Images of Metal Structures

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