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texture image segmentation using Vonn mixtures-based hidden Markov tree model and relative phase

MULTIMEDIA TOOLS AND APPLICATIONS 2020年第39-40期79卷 29799-29824页

作者： Niu, Pan-pan Wang, Li Shen, Xin Wang, Qian Wang, Xiang-yang Liaoning Normal Univ Sch Comp & Informat Technol Dalian 116029 Peoples R China

texture segmentation is a frequently occurring and challenging problem in many computer vision and pattern recognition applications. The importance of phase information for texture analysis has been earlier established for many image processing. Undecimated dual tree complex wavelet transform (UDTCWT) is a new image decomposition. It not only provides exact translational invariance and rich directional selectivity, but also offers perfect consistent relative phase relationships across scales. In this paper, we propose a novel texture image segmentation framework using Vonn mixtures-based hidden Markov trees (HMT) and UDTCWT domain relative phase. Firstly, we analyze the robustness and marginal distribution of UDTCWT relative phases, and various strong dependencies between UDTCWT relative phases. Then, we propose a new HMT statistical model in UDTCWT domain, namely Vonn mixtures-based HMT, by describing the UDTCWT relative phases statistical distribution with Vonn mixtures (VM), which can capture both the subband marginal distributions and the strong dependencies across scales of the UDTCWT relative phases. Finally, we develop a texture image segmentation framework using the Vonn mixtures-based HMT model of UDTCWT domain relative phases, in which expectation-maximization (EM) parameter estimation, Bayesian multiscale raw segmentation, and context based multiscale fusion are used. Comparing to the state-of-the-art techniques, the proposed method can not only produce high-quality segmentation results in a more efficient way, but also keep a lot of boundary details in the segmentation results.

关键词： texture image segmentation Undecimated dual tree complex wavelet transform Relative phase Vonn probability density function Hidden Markov tree

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texture image segmentation Based on Classification of Contour Elements and Logical Addition of Classes

Texture Image Segmentation Based on Classification of Contou...

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Al-Sadiq International Conference on Multidisciplinary in IT and Communication Techniques Science and Applications (AIC-MITCSA)

作者： Alzakki, Hydear Tsviatkou, Viktar BSUIR Dept Telecommun & Network Devices Minsk BELARUS

ISBN: (纸本)9781509032471

A method for texture images segmentation based on classification of contour elements and logical addition of classes. The essence of the method consists in the contouring of the image, determining the position of contour elements in the image of different types (points, lines, and shapes) converting closely spaced similar contour elements into binary regions objects, binary coding mutual position obtained areal objects within the boundaries of the image segmentation are resulting in code matrix.

关键词： texture image segmentation classification contour elements

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texture image segmentation Based on Classification of Contour Elements and Logical Addition of Classes

Texture Image Segmentation Based on Classification of Contou...

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Al-Sadeq International Conference on Multidisciplinary in IT and Communication Science and Applications

作者： Hydear Alzakki Viktar Tsviatkou Dept. of Telecommunication and Network Devices BSUIR

ISBN: (纸本)9781509032488

关键词： texture image segmentation Classification contour elements

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texture image segmentation Using Spectral Clustering 17th

Texture Image Segmentation Using Spectral Clustering

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2nd International Conference on Learning and Collaboration Technologies / 17th International Conference on Human-Computer Interaction

作者： Du, Hui Wang, Yuping Dong, Xiaopan Cheung, Yiu-ming Xidian Univ Sch Comp Sci & Technol Xian 710071 Peoples R China Northwest Normal Univ Coll Comp Sci & Engn Lanzhou Gansu Peoples R China Hong Kong Baptist Univ Dept Comp Sci Kowloon Tong Hong Kong Peoples R China

ISBN: (纸本)9783319213804;9783319213798

Clustering is a popular and effective method for texture image segmentation. However, most cluster methods often suffer the following problems: need a huge space and a lot of computation when the input data is large. To save the space and computation, we construct a novel algorithm for image segmentation. It consists of two phases: Sampling and clustering. First, we put some detectors into the data space uniformly using orthogonal design method. These detectors can move and merge according to the law of universal gravitation. When the detectors are in a stable status (i.e., do not move), these detectors are used as the representative samples to the next step. Second, to further improve the efficiency and avoid dependence on parameters, the Self-tuning Spectral Clustering (SSC) is used to the representative samples to do the clustering. As a result, the proposed algorithm can quickly and precisely realize the clustering for texture image segmentation.

关键词： texture image segmentation Feature extraction Gray level histogram Sampling Spectral clustering

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texture image segmentation Using Affinity Propagation and Spectral Clustering

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2015年第5期29卷 1555009-1555009页

作者： Du, Hui Wang, Yuping Dong, Xiaopan Xidian Univ Sch Comp Sci & Technol Xian 710071 Shaanxi Peoples R China Northwest Normal Univ Coll Comp Sci & Engn Lanzhou 710070 Gansu Peoples R China

Clustering is a popular and effective method for image segmentation. However, existing cluster methods often suffer the following problems: (1) Need a huge space and a lot of computation when the input data are large. (2) Need to assign some parameters (e.g. number of clusters) in advance which will affect the clustering results greatly. To save the space and computation, reduce the sensitivity of the parameters, and improve the effectiveness and efficiency of the clustering algorithms, we construct a new clustering algorithm for image segmentation. The new algorithm consists of two phases: coarsening clustering and exact clustering. First, we use Affinity Propagation (AP) algorithm for coarsening. Specifically, in order to save the space and computational cost, we only compute the similarity between each point and its t nearest neighbors, and get a condensed similarity matrix (with only t columns, where t << N and N is the number of data points). Second, to further improve the efficiency and effectiveness of the proposed algorithm, the Self-tuning Spectral Clustering (SSC) is used to the resulted points (the representative points gotten in the first phase) to do the exact clustering. As a result, the proposed algorithm can quickly and precisely realize the clustering for texture image segmentation. The experimental results show that the proposed algorithm is more efficient than the compared algorithms FCM, K-means and SOM.

关键词： texture image segmentation feature extraction gray level histogram affinity propagation spectral clustering

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Active Contour Model based on LTP code for texture image segmentation

Active Contour Model based on LTP code for Texture Image Seg...

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International Conference on Information Science, Electronics and Electrical Engineering (ISEEE)

作者： Chen, Guannan Liu, Yao Gong, Haiming Li, Yan Chen, Rong Fujian Normal Univ Key Lab Optoelect Sci & Technol Med Minist Educ Fuzhou Peoples R China

ISBN: (纸本)9781479931972

In this paper, a novel algorithm is proposed for unsupervised segmentation that incorporates a Local Trinary Pattern (LTP) code representation of textures under a geometric active contour framework. First, by combining the gray levels of pixels with texture information of an image, this method can be used for segmentation of a texture image or a non-texture image. And then, the method is modified to avoid the additional computation problem without re-initialization repeatedly. The simulation experiments show that the proposed segmentation method is more efficient and accurate.

关键词： texture image segmentation Level set method Local Trinary Pattern Active contour model

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Sonar image MRF segmentation Algorithm Based on texture Feature Vector

Sonar Image MRF Segmentation Algorithm Based on Texture Feat...

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Global OCEANS Singapore - U.S. Gulf Coast Conference

作者： Li Luyuan Wang Huigang Northwestern Polytech Univ Sch Marine Sci & Technol Xian Peoples R China

ISBN: (数字)9781728154466

ISBN: (纸本)9781728154466

In side scan sonar image processing, the target area and the shadow area are important features of the underwater target. Segmenting it from a background image is an important step in subsequent image recognition. However, the common segmentation algorithms have serious background interference, serious loss of edge contour information, and inconsistent target area and shadow area. Aiming at these shortcomings, this paper proposes an improved Markov random field (MRF) based texture image segmentation algorithm. The feature vector field is used instead of the original MRF observation field, where the feature vectors are obtained by the gray level co-occurrence matrix, the fractional differential operation and the Zipf' s law. In this paper, we compare the results of using three algorithms together and three algorithms respectively on the real side scan sonar image. It turns out that using the three methods together, that is, using the integrated feature vector, a more ideal segmentation effect can be obtained.

关键词： side scan sonar image texture image segmentation gray level co-occurrence matrix fractional differential operation Zipf's law

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texture image segmentation Based On M-band Wavelet Derived Features Using Fuzzy C-Means

Texture Image Segmentation Based On M-band Wavelet Derived F...

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International Conference on Information Communication and Embedded Systems (ICICES)

作者： Priya, A. Devi Kumar, G. Veera Senthil Velammal Coll Engn & Technol Madurai Tamil Nadu India

ISBN: (纸本)9781467357869;9781467357876

This paper presents a scheme for segmentation of texture images combining M-band wavelet transform and Fuzzy C-Means. M-band wavelet transform yields a large number of sub images which enhances the performance. M-Band Wavelet transform decomposes an image in to MxM channels. Different combinations of these band pass sections produce various scales and orientations in frequency plane, hence it produces a sixteen sub-band images. These features are subjected to Fuzzy C-Means clustering technique for segmentation. The advantage of FCM is that it does not require a priori knowledge to segment a region. This new combined algorithm produces good segmentation results by applying FCM for M-Band Wavelet extracted features.

关键词： texture image segmentation M-Band Wavelet transform local energy estimator Fuzzy C-Means Clustering

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texture image segmentation Based On M-band Wavelet Derived Features Using Fuzzy C-Means

Texture Image Segmentation Based On M-band Wavelet Derived F...

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International Conference on Information Communication and Embedded Systems

作者： A. Devi Priya G. Veera Senthil Kumar P. G Student Velammal College of Engineering and Technology Madurai Assistant Professor Velammal College of Engineering and Technology Madurai

ISBN: (纸本)9781467357869

关键词： texture image segmentation M-Band Wavelet transform local energy estimator Fuzzy C-Means Clustering

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Interactive segmentation of texture image based on active contour model with local inverse difference moment feature

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MULTIMEDIA TOOLS AND APPLICATIONS 2018年第18期77卷 24537-24564页

作者： Zhao, Guo Qin, Shiyin Wang, Danyang Beihang Univ Sch Automat Sci & Elect Engn 37 Xueyuan Rd Beijing 100191 Peoples R China

texture image segmentation is a challenging problem in image processing field due to wide variability of characterizing textures and a lack of proper contour information. In this paper, an effective presentation is found with which different textures can be represented with some corresponding distributions generated by feature values of local inverse difference moment (LIDM). By the analysis of local statistical information in gray level co-occurrence matrix (GLCM), we found that similar textures can be characterized with similar distributions. In this way, an interactive segmentation method is presented to achieve the segmentation of texture image based on GLCM with an optimizing model. Our scheme can be narrated separately as follows, firstly, a proper Gaussian kernel is selected to discriminate two classes of textures by analyzing LIDM feature distributions, which are obtained from two different local regions marked manually in the initial texture image. Secondly, the LIDM feature map can be constructed by computing LIDM feature values of image patches with the proper Gaussian kernel, and the center of these image patches may traverse the whole image domain. Finally, the texture image segmentation is implemented based on an improved optimizing model with local binary fitting and local extremum regularizing. In order to validate the performance of our proposed method, two kinds of experiments about discriminative feature map construction and texture image segmentation are carried out to demonstrate its well performance, and more experiments on real texture images are also conducted.

关键词： Gray level co-occurrence matrix Active contour model texture image segmentation Local inverse difference moment

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