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unsupervised segmentation of hidden semi-Markov non-stationary chains

SIGNAL PROCESSING 2012年第1期92卷 29-42页

作者： Lapuyade-Lahorgue, Jerome Pieczynski, Wojciech CNRS UMR 5157 Dept CITI Inst Telecom F-91000 Evry France

The Bayesian segmentation using Hidden Markov Chains (HMC) is widely used in various domains such as speech recognition, acoustics, biosciences, climatology, text recognition, automatic translation and image processing. On the one hand, hidden semi-Markov chains (HSMC), which extend HMC, have turned out to be of interest in many situations and have improved HMC-based results. On the other hand, the case of non-stationary data can pose an important problem in real-life situations, especially when the model parameters have to be estimated. The aim of this paper is to consider these two extensions simultaneously: we propose using a particular triplet Markov chain (TMC) to deal with non-stationary hidden semi-Markov chains. In addition, we consider a recent particular HSMC having the same computation complexity as the classical HMC. We propose a related parameter estimation method and the resulting unsupervised Bayesian segmentation is validated through experiments;in particular, a real radar image segmentations are provided. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Non-stationary hidden semi-Markov chain unsupervised segmentation Iterative conditional estimation Triplet Markov chains

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unsupervised segmentation of highly dynamic scenes through global optimization of multiscale cues

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PATTERN RECOGNITION 2015年第11期48卷 3477-3487页

作者： Zhang, Yinhui Abdel-Mottaleb, Mohamed He, Zifen Kunming Univ Sci & Technol Fac Mech & Elect Engn Chenggong 650500 Kunming Peoples R China Univ Miami Dept Elect & Comp Engn Coral Gables FL 33146 USA Effat Univ Jeddah Saudi Arabia

We propose a novel method for highly dynamic scene segmentation by formulating foreground object extraction as a global optimization framework that integrates a set of multiscale spatio-temporal cues. The multiscale features consist of a combination of motion and spectral components at a pixel level as well as spatio-temporal consistency constraints between superpixels. To compensate for the ambiguities of foreground hypothesis due to highly dynamic and cluttered backgrounds, we formulate salient foreground mapping as a convex optimization of weighted total variation energy, which is efficiently solved by using an alternating minimization scheme. Moreover, the appearance and position spatio-temporal consistency constraints between superpixels are explicitly incorporated into a Markov random field energy functional for further refinement of the set of salient pixel-level foreground mapping. This work facilitates sequential integration of multiscale probability constraints into a global optimal segmentation framework to help address object boundary ambiguities in the case of highly dynamic scenes. Extensive experiments on challenging dynamic scene data sets demonstrate the feasibility and superiority of the proposed segmentation approach. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Image sequence segmentation Dynamic scene unsupervised segmentation Global optimization

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unsupervised segmentation and elm for fabric defect image classification

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MULTIMEDIA TOOLS AND APPLICATIONS 2019年第9期78卷 12421-12449页

作者： Liu, Li Zhang, Jianhong Fu, Xiaodong Liu, Lijun Huang, Qingsong Kunming Univ Sci & Technol Fac Informat Engn & Automat Comp Technol Applicat Key Lab Yunnan Prov Kunming 650500 Yunnan Peoples R China Kunming Univ Sci & Technol Fac Aeronaut Kunming 650500 Yunnan Peoples R China Kunming Univ Sci & Technol Fac Informat Engn & Automat Kunming 650500 Yunnan Peoples R China

In order to solve the problem of low accuracy and efficiency for surface defects in common woven fabrics, a novel fabric defect classification method is proposed based on unsupervised segmentation and ELM. The classification method is divided into four steps including defect segmentation, feature extraction, ELM classifier training, and Bayesian probability fusion. Firstly, an unsupervised segmentation is presented for the Grayscale fabric defect image after preprocessing. Secondly, geometric and texture features were extracted by using the segmented image and the undivided Grayscale image. Then, features and labels in fabric defect images are considered as training sets to train the ELM classifier. Finally, the input fabric defect image is classified by the trained ELM classifier and the Bayesian probability fusion method. Experimental results show that the proposed method can classify the fabric defect image with high accuracy and efficiency that can better meet the requirements for practical applications.

关键词： Fabric defect detection Image classification unsupervised segmentation ELM classifier Bayesian probability fusion

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unsupervised segmentation of Human Manipulation Movements Into Building Blocks

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IEEE ACCESS 2022年 10卷 125723-125734页

作者： Gutzeit, Lisa Kirchner, Frank Univ Bremen Robot Res Grp D-28359 Bremen Germany DFKI Robot Innovat Ctr D-28359 Bremen Germany

During the last years, new approaches were proposed in which robotic behavior is generated by imitating human movement examples. This process can be sustainably simplified by an automatic detection of the movement sequences which should be imitated. For this, automated approaches for human movement segmentation are needed to avoid time-intensive manual data analysis. Suitable examples for imitation learning are building block movements, which are basic movements that can be combined to solve different tasks. Recently, we introduced the velocity-based Multiple Change-point Inference (vMCI) algorithm, which automatically segments human demonstrations of manipulation movements into sequences with a bell-shaped velocity of the hand which is said to be a characteristic feature of manipulation building blocks. In this paper, the velocity of the hand as well as other features of human manipulation movements recorded with a marker-based motion tracking system are evaluated with respect to their suitability to detect segment boundaries of manipulation building blocks. Additionally, we perform a more intensive evaluation of vMCI compared to the original publication by evaluating the algorithm on different manipulation movement demonstrations recorded from several subjects and comparing the approach to other state-of-the-art segmentation algorithms. The results support the assumption that the velocity of the hand is one of the main features to detect segment boundaries in human manipulation movements and that the vMCI algorithm can detect these segment borders online and unsupervised, also in movement recordings with a noisy velocity.

关键词： Human movement analysis movement segmentation unsupervised segmentation time series segmentation

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unsupervised segmentation based on robust estimation and color active contour models

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IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE 2005年第3期9卷 475-486页

作者： Yang, L Meer, P Foran, DJ Rutgers State Univ Dept Elect & Comp Engn Piscataway NJ 08854 USA UMDNJ Robert Wood Johnson Med Sch Ctr Biomed Imaging & Informat Piscataway NJ 08854 USA

One of the most commonly used clinical tests performed today is the routine evaluation of peripheral blood smears. In this paper, we investigate the design, development, and implementation of a robust color gradient vector flow (GVF) active contour model for performing segmentation, using a database of 1791 imaged cells. The algorithms developed for this research operate in Luv color space, and introduce a color gradient and L2E robust estimation into the traditional GVF snake. The accuracy of the new model was compared with the segmentation results using a mean-shift approach, the traditional color GVF snake, and several other commonly used segmentation strategies,. The unsupervised robust color snake with L2E robust estimation was shown to provide results which were superior to the other unsupervised approaches, and was comparable with supervised segmentation, as judged by a panel of human experts.

关键词： active contours image segmentation L2E robust estimation unsupervised segmentation

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unsupervised segmentation of medical image based on difference of mutual information

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Science in China(Series F) 2006年第4期49卷 484-493页

作者： LU Qingwen CHEN Wufan School of BME Southern Medical University Guangzhou 510515 China

In the scope of medical image processing, segmentation is important and difficult. There are still two problems which trouble us in this field. One is how to determine the number of clusters in an image and the other is how to segment medical images containing lesions. A new segmentation method called DDC, based on difference of mutual information （dMI） and pixon, is proposed in this paper. Experiments demonstrate that dMI shows one kind of intrinsic relationship between the segmented image and the original one and so it can be used to well determine the number of clusters. Furthermore, multi-modality medical images with lesions can be automatically and successfully segmented by DDC method.

关键词： medical image unsupervised segmentation mutual information pixon.

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unsupervised segmentation of Hyperspectral Images Using 3-D Convolutional Autoencoders

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2020年第11期17卷 1948-1952页

作者： Nalepa, Jakub Myller, Michal Imai, Yasuteru Honda, Ken-Ichi Takeda, Tomomi Antoniak, Marek Silesian Tech Univ Gliwice Poland KP Labs Gliwice Poland Kokusai Kogyo Co Ltd Tokyo 1020085 Japan Japan Space Syst Tokyo 1050011 Japan

Hyperspectral image analysis has become an important topic widely researched by the remote sensing community. Classification and segmentation of such imagery help understand the underlying materials within a scanned scene since hyperspectral images convey detailed information captured in a number of spectral bands. Although deep learning has established the state-of-the-art in the field, it still remains challenging to train well-generalizing models due to the lack of ground-truth data. In this letter, we tackle this problem and propose an end-to-end approach to segment hyperspectral images in a fully unsupervised way. We introduce a new deep architecture which couples 3-D convolutional autoencoders with clustering. Our multifaceted experimental study-performed over the benchmark and real-life data-revealed that our approach delivers highquality segmentation without any prior class labels.

关键词： Autoencoder clustering deep learning hyperspectral imaging (HSI) unsupervised segmentation

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unsupervised segmentation of Markov Random Fields Corrupted by Nonstationary Noise

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IEEE SIGNAL PROCESSING LETTERS 2016年第11期23卷 1607-1611页

作者： Habbouchi, Ahmed Boudaren, Mohamed El Yazid Aissani, Amar Pieczynski, Wojciech Ecole Mil Polytech Algiers 16111 Algeria Univ Sci & Technol Houari Boumed Algiers 16111 Algeria Univ Paris Saclay CNRS Telecom SudParis SAMOVAR 9 Rue Charles Fourier F-91000 Evry France

HiddenMarkov fields have been widely used in image processing thanks to their ability to characterize spatial information. In such models, the process of interest X is hidden and is to be estimated from an observable process Y. One common way to achieve the associated inference tasks is to define, on one hand, the prior distribution p(x);and on the other hand, the noise distribution p(y vertical bar x). While it is commonly established that the prior distribution is given by a Markov random field, the noise distribution is usually given through a set of Gaussian densities;one per each label. Hence, observed pixels belonging to the same class are assumed to be generated by the same Gaussian density. Such assumption turns out, however, to be too restrictive in some situations. For instance, due to light conditions, pixels belonging to a same label may present quite different visual aspects. In this letter, we overcome this drawback by considering an auxiliary field U in accordance with the triplet Markov field formalism. Experimental results on simulated and real images demonstrate the interest of the proposed model with respect to the common hidden Markov fields.

关键词： Hidden Markov fields (HMFs) nonstationary noise triplet Markov fields (TMFs) unsupervised segmentation

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unsupervised segmentation of Random Discrete Data Hidden With Switching Noise Distributions

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IEEE SIGNAL PROCESSING LETTERS 2012年第10期19卷 619-622页

作者： Boudaren, Mohamed El Yazid Monfrini, Emmanuel Pieczynski, Wojciech Ecole Mil Polytech Algiers 16111 Algeria TELECOM SudParis Inst Mines Telecom Dept CITI CNRSUMR 5157 F-91011 Evry France Univ Sci & Technol Houari Boumediene Algiers 16111 Algeria

Hidden Markov models are very robust and have been widely used in a wide range of application fields;however, they can prove some limitations for data restoration under some complex situations. These latter include cases when the data to be recovered are nonstationary. The recent triplet Markov models have overcome such difficulty thanks to their rich formalism, that allows considering more complex data structures while keeping the computational complexity of the different algorithms linear to the data size. In this letter, we propose a new triplet Markov chain that allows the unsupervised restoration of random discrete data hidden with switching noise distributions. We also provide genuine parameters estimation and MPM restoration algorithms. The new model is validated through experiments conducted on synthetic data and on real images, whose results show its interest with respect to the standard hidden Markov chain.

关键词： Hidden Markov chains switching noise distributions triplet Markov chains unsupervised segmentation

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Novel unsupervised Disease segmentation Framework Based on Clustering 8th

Novel Unsupervised Disease Segmentation Framework Based on C...

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8th National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics-NCVPRIPG

作者： Singh, Rama Kant Aggarwal, Monika Lall, Brejesh Indian Inst Technol Delhi Bharti Sch New Delhi India Indian Inst Technol Delhi CARE New Delhi India Indian Inst Technol Delhi Elect Engg Dept New Delhi India

ISBN: (纸本)9789819752119;9789819752126

Plant disease is a major limiting factor in yield production. Traditionally, biologists rely on manual screening (localisation) of disease symptoms exhibited on plant leaves. However, this is a highly subjective and error-prone analysis. With the advancement of imaging devices, image-based analysis permits high-throughput disease localisation. In this context, state-of-the-art supervised deep learning-based methods have been presented. However, annotation of disease symptoms is time-consuming and is a current bottleneck. To relieve this limitation, we propose a novel unsupervised framework that utilises Cluster weight as well as Group-local Feature-weight learning using iteration in Fuzzy C-Means (CGFFCM) guided via differentiable clustering algorithm for accurate disease localisation. It is to be noted that the proposed framework does not rely on annotated image data. In this paper, we also performed an exhaustive comparison based on different clustering methods to show the efficacy of the proposed framework.

关键词： Disease phenotyping unsupervised segmentation Differentiable clustering Fuzzy C-means CGFFCM

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