The Experts below are selected from a list of 184734 Experts worldwide ranked by ideXlab platform
Ying Sun - One of the best experts on this subject based on the ideXlab platform.
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ICDSP - Image Segmentation Technology Based on Genetic Algorithm
Proceedings of the 2019 3rd International Conference on Digital Signal Processing - ICDSP 2019, 2019Co-Authors: Chong Tan, Ying Sun, Bo Tao, Fei ZengAbstract:Image Segmentation technology is one of the important topics in the field of digital Image research. However, there is no uniform standard for existing Image Segmentation methods, and the traditional Image Segmentation method is only suitable for some specific occasions. Therefore, it is very urgent to research and develop new theories and methods of Image Segmentation technology. Genetic algorithm is a method for calculating the optimal solution by simulating the biological evolution process in the natural selection and genetic mechanism of biological evolution. It has strong robustness, parallelism, adaptability and fast convergence. It can be applied in Image Segmentation technology to determine the Segmentation threshold. Therefore, this paper studies the Image Segmentation based on genetic algorithm, and compares different Image Segmentation algorithms. The experimental results show that the Image Segmentation effect based on genetic algorithm is better than the traditional Image Segmentation.
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ICMLC - Image Segmentation Algorithm Based On Clustering
2018 International Conference on Machine Learning and Cybernetics (ICMLC), 2018Co-Authors: Ying Sun, Bo Tao, Jianyi KongAbstract:Image Segmentation plays an important role in Image processing. Image Segmentation algorithms have been proposed as early as the last century, and constantly find and optimize various algorithms. The quality of the Image Segmentation algorithm determines the result of Image analysis and Image understanding. The principle, advantages and disadvantages of traditional Image Segmentation algorithms are briefly introduced in this paper. The variety of Image Segmentation algorithms is determined by the complexity of the Image itself. In recent years, scholars continue to improve a variety of Image Segmentation algorithms, the paper introduces the improvement of fuzzy C-means algorithm and mean-shift algorithm. The fuzzy C-means algorithm does not consider the spatial information of the Image. Put forward an fuzzy C-means algorithm based on membership correction is proposed, taking into account the high correlation of pixels in Image Segmentation. The mean shift algorithm converges slowly, and mean shift algorithm based on conjugate gradient method is proposed to improve the convergence speed of the algorithm.
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color Image Segmentation advances and prospects
Pattern Recognition, 2001Co-Authors: H D Cheng, Ying Sun, Xihua Jiang, Jingli WangAbstract:Abstract Image Segmentation is very essential and critical to Image processing and pattern recognition. This survey provides a summary of color Image Segmentation techniques available now. Basically, color Segmentation approaches are based on monochrome Segmentation approaches operating in different color spaces. Therefore, we first discuss the major Segmentation approaches for segmenting monochrome Images: histogram thresholding, characteristic feature clustering, edge detection, region-based methods, fuzzy techniques, neural networks, etc.; then review some major color representation methods and their advantages/disadvantages; finally summarize the color Image Segmentation techniques using different color representations. The usage of color models for Image Segmentation is also discussed. Some novel approaches such as fuzzy method and physics-based method are investigated as well.
Xiangchu Feng - One of the best experts on this subject based on the ideXlab platform.
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A multiscale Image Segmentation method
Pattern Recognition, 2016Co-Authors: Xiangchu FengAbstract:This paper presents a novel Image Segmentation framework that combines Image Segmentation and feature extraction into a unified model. The proposed model consists of two parts: the Segmentation part and the multiscale decomposition part. In the model, the Segmentation part relies on the Image intensities in the regions of interest while the multiscale decomposition part depends on the features in different scales. The multiscale decomposition facilitates the process of Segmentation since the region of interest can be easily detected from a proper scale. The total variation projection regularization (TVPR) is used to preserve geometric shape of the segmented regions. According to the geometric significance of TVPR parameters, an adaptive TVPR parameters selection method is presented and edges of different region can be well preserved. The proposed method is able to deal with intensity inhomogeneities and mixed noises often occurred in real-world Images, which present challenges in Image Segmentation. Numerical examples on synthetic and real Images are given to demonstrate the effectiveness of the proposed method. This paper proposes a novel Image Segmentation framework.A multiscale Image Segmentation method is presented within our framework.Total variation projection regularization (TVPR) is used to the proposed model.We present an adaptive TVPR parameters selection method for Image Segmentation.The experimental results show the effectiveness of the proposed method.
Jingli Wang - One of the best experts on this subject based on the ideXlab platform.
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color Image Segmentation advances and prospects
Pattern Recognition, 2001Co-Authors: H D Cheng, Ying Sun, Xihua Jiang, Jingli WangAbstract:Abstract Image Segmentation is very essential and critical to Image processing and pattern recognition. This survey provides a summary of color Image Segmentation techniques available now. Basically, color Segmentation approaches are based on monochrome Segmentation approaches operating in different color spaces. Therefore, we first discuss the major Segmentation approaches for segmenting monochrome Images: histogram thresholding, characteristic feature clustering, edge detection, region-based methods, fuzzy techniques, neural networks, etc.; then review some major color representation methods and their advantages/disadvantages; finally summarize the color Image Segmentation techniques using different color representations. The usage of color models for Image Segmentation is also discussed. Some novel approaches such as fuzzy method and physics-based method are investigated as well.
Fengxiangchu - One of the best experts on this subject based on the ideXlab platform.
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A multiscale Image Segmentation method
Pattern Recognition, 2016Co-Authors: Liyafeng, FengxiangchuAbstract:This paper presents a novel Image Segmentation framework that combines Image Segmentation and feature extraction into a unified model. The proposed model consists of two parts: the Segmentation par...
Terry M. Peters - One of the best experts on this subject based on the ideXlab platform.
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The semiotics of medical Image Segmentation.
Medical image analysis, 2017Co-Authors: John S. H. Baxter, Eli Gibson, Roy Eagleson, Terry M. PetersAbstract:As the interaction between clinicians and computational processes increases in complexity, more nuanced mechanisms are required to describe how their communication is mediated. Medical Image Segmentation in particular affords a large number of distinct loci for interaction which can act on a deep, knowledge-driven level which complicates the naive interpretation of the computer as a symbol processing machine. Using the perspective of the computer as dialogue partner, we can motivate the semiotic understanding of medical Image Segmentation. Taking advantage of Peircean semiotic traditions and new philosophical inquiry into the structure and quality of metaphors, we can construct a unified framework for the interpretation of medical Image Segmentation as a sign exchange in which each sign acts as an interface metaphor. This allows for a notion of finite semiosis, described through a schematic medium, that can rigorously describe how clinicians and computers interpret the signs mediating their interaction. Altogether, this framework provides a unified approach to the understanding and development of medical Image Segmentation interfaces.
