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Texture Segmentation Based on image Model and Neural Network

Journal of Earth Science 1999年第4期18卷 333-335页

作者： Chen Zhenyu Sheng Wen Wang Guangjun Li Dehua(State Education Commission Laboratory for image processing and Intelligence Control, institute for pattern recognition and Artificial Intelligence, Huazhong university of Science and technology, Wu han 430074) State Education Commission Laboratory for Image Processing and Intelligence Control Institute for Pattern Recognition and Artificial IntelligenceHuazhong University of Science and TechnologyWuhan 430074

This paper presents a texture segmentation approach which is based on the Markov random field model (MRF) and feed forward neural *** texture is modeled by the second order Gauss MRF model, and the least square error estimation is employed for the solution of model parameters. To perform texture segmentation, we introduced an improved BP algorithm to get faster learning speed. Experiment shows that better segmentation results can be obtained than the traditional Euclidean distance method.

关键词： texture analysis Markov random field neural network

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Part-Based Tracking with Appearance Learning and Structural Constrains

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2014年 8834卷 594-601页

作者： Xiang, Wei Zhou, Yue Institute of Image Processing and Pattern Recognition Jiao Tong University Shanghai China

Adaptive tracking-by-detection methods are widely used in computer vision for tracking objects. Despite these methods achieve promising results, deformable targets and partial occlusions continue to represent key problem in visual tracking. In this paper, we propose a part-based visual tracking method. First, we take advantage of the existing online learning appearance model to learning the appearance of each part. Second, we propose a novel part initialization method and an affine invariant structural constrain between these parts. Third, a tracking model based on the appearance of each part and the spatial relationship between the parts is proposed. We make use of an optimization algorithm to find the best parts during tracking, update the appearance model and the structural constraints between parts simultaneously. In this paper we show our method has many advantages over the pure appearance learning based tracking model. Our method can effective solve the partial occlusion problem, and relieve the drift problems. What’s more, our method achieves great result while tracking the target of which geometric appearance changes drastically over time. © Springer International Publishing Switzerland 2014.

关键词： Target tracking

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Multiple model particle filter with correlated measurement and process noise

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Journal of Information and Computational Science 2013年第4期10卷 1179-1186页

作者： Hu, Zhentao Li, Song Liu, Xianxing Li, Jie Institute of Image Processing and Pattern Recognition Henan University Kaifeng 475004 China

Aiming at adverse influence of the correlation between measurement and process noise for filtering precision, a new multiple model particle filtering algorithm with correlated measurement noise and process noise is proposed. Firstly, multiple model particle filter is used as the basic framework realization of new algorithm, and the objective is to deal with the multi-mode problem in estimated system with less the calculated amount. Secondly, in order to avoid adverse influence from the correlation between measurement and process noise for filtering precision, the system model is modified by rearrange the state transition equation and measurement equation, and a novel decoupling method of correlated noise is given. Finally, experimental results show that the proposed method outperforms the general IMM-PF and MMPF when the measurement and process noise is with correlated characteristics. © 2013 by Binary Information Press.

关键词： Equations of state

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Semi-supervised Kernel Minimum Squared Error based on manifold structure

Semi-supervised Kernel Minimum Squared Error based on manifo...

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10th International Symposium on Neural Networks, ISNN 2013

作者： Gan, Haitao Sang, Nong Chen, Xi Science and Technology on Multi-spectral Information Processing Laboratory Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology 430074 China

ISBN: (纸本)9783642390647

Kernel Minimum Squared Error (KMSE) has been receiving much attention in data mining and pattern recognition in recent years. Generally speaking, training a KMSE classifier, which is a kind of supervised learning, needs sufficient labeled examples. However, there are usually a large amount of unlabeled examples and few labeled examples in real world applications. In this paper, we introduce a semi-supervised KMSE algorithm, called Laplacian regularized KMSE (LapKMSE), which explicitly exploits the manifold structure. We construct a p nearest neighbor graph to model the manifold structure of labeled and unlabeled examples. Then, LapKMSE incorporates the structure information of labeled and unlabeled examples in the objective function of KMSE by adding a Laplacian regularized term. As a result, the labels of labeled and unlabeled examples vary smoothly along the geodesics on the manifold. Experimental results on several synthetic and real-world datasets illustrate the effectiveness of our algorithm. © 2013 Springer-Verlag Berlin Heidelberg.

关键词： Supervised learning

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A clique of active appearance models by minimum description length

A clique of active appearance models by minimum description ...

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2005 16th British Machine Vision Conference, BMVC 2005

作者： Langs, Georg Peloschek, Philipp Donner, René Bischof, Horst Institute for Computer Graphics and Vision Graz University of Technology Austria Department of Diagnostic Radiology Medical University of Vienna Austria Pattern Recognition and Image Processing Group Vienna University of Technology Austria

ISBN: (纸本)1901725294

Autonomous model building is a crucial trend in model based methods like AAMs. This paper introduces an approach that deals with non-linearities by detecting distinct sub-parts in the data. Sub-models each representing an individual sub-part are derived from a minimum description length criterion. Thereby the resulting clique of models is more compact and obtains a better generalization behavior than a single model. The proposed AAM clique generation deals with non-linearities in the data in a generic information theoretic manner reducing the necessity of user interaction during training.

关键词： Information theory

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Marginalized particle filtering algorithm in correlated noise

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Journal of Computational Information Systems 2013年第1期9卷 17-24页

作者： Hu, Zhentao Yang, Yiping Li, Jie Liu, Xianxing Institute of Image Processing and Pattern Recognition Henan University Kaifeng 475004 China

The particle filter offers a general numerical tool to approximate the posterior density function for the state in nonlinear and non-Gaussian filtering problems. While the particle filter is fairly easy to implement and tune, its main drawback is that its computational complexity increase quickly with the state dimension. By exploiting conditional dependencies between parts of the state to estimate, marginalized particle filter can improve the estimation quality while also reducing the overall computational load relative to standard PF. However, whether marginalized particle filter or other improving particle filters, process noise and observation noise are all supposed as the independent identical distribution. It will limit greatly the application field of sampling nonlinear filter. Aiming at the above problem, a decouple method of noise is structured by the rearrange and transformation of state transition equation and observation equation. Meanwhile, the decouple method is dynamically introduced into the framework of marginalized particle filter. Because the treatment for the computation-al complexity and correlation of noise are considered, the real-time and the filtering precision are obviously improved. Finally, the theoretical analysis and experimental results show the feasibility and efficiency of the proposed algorithm. © 2013 Binary Information Press.

关键词： Bandpass filters

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Locally adaptive shearlet denoising based on bayesian MAP estimate

Locally adaptive shearlet denoising based on bayesian MAP es...

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International Conference on image and Graphics

作者： Dan, Zhiping Chen, Xi Gan, Haitao Gao, Changxin Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan China Institute of Intelligent Vision and Image Information China Three Gorges University Yichang China

ISBN: (纸本)9780769545417

A locally adaptive Bayesian estimate for image denoising is proposed by exploiting the correlation among image shearlet coefficients in a sub-band. The Laplacian distribution can model a wide range of process, from heavy-tailed to less heavy-tailed processes. This paper deduces Laplacian prior distribution based the MAP estimate formula and sub-band adaptive threshold. Finally, a simulation is carried out to show the effectiveness of the new estimate. Experiment results demonstrate that compared with classical sub-band adaptive algorithms, the new denoising method has significantly increased peak signal-to-noise ratio (PSNR) and improved the quality of subjective visual effect. © 2011 IEEE.

关键词： Adaptive algorithms

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An Self-supervised Learning Self-attention Based Method for Defects Classification on PCB Surface images 2

An Self-supervised Learning Self-attention Based Method for ...

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2nd International Conference on Electronics, Communications and Information technology, CECIT 2021

作者： Zhou, Lijie Ling, Xufeng Zhu, Shan Sun, Zheng Yang, Jie AI School Tianhua College Shanghai Normal University Shanghai China ASIC Motor Corporation Limited China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

ISBN: (纸本)9781665437578

Automatic detection of PCB defects become a difficult work in electronics industry, with the rapid development of Integrated Circuit. A good detection method can effectively improve production efficiency and reduce the costs of quality inspection. The traditional image processing methods have the backwards of complicated system structure, difficult image segmentation, and low detection accuracy, etc. In order to solve the above problems, this paper proposes a method of extracting features by self-supervised learning with supervised learning to complete the classification task. This method uses a deep neural network model based on self-supervised learning technology, uses multi-head self-attention mechanism;automatically recognize single or multiple defect areas in PCB, to extract defects features of PCB, and uses full connection classifier to classify features. The open-source (DeepPCB) is selected as the dataset, including 6 defect categories and non-defect categories. In the training process, data enhancement technology is used to increase the training samples, train the model and make it converge, and realize low error recognition on the verification dataset. The experimental results show that the features extracted by the self-supervised learning method are richer than those extracted by the supervised learning. The composite model formed by combining the self-attention mechanism has high accuracy and good robustness, reaches the classification accuracy of 94.33%, and has high accuracy and good generalization ability. © 2021 IEEE.

关键词： Deep neural networks

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Convolution neural network for automatic facial expression recognition

Convolution neural network for automatic facial expression r...

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2017 IEEE International Conference on Applied System Innovation, ICASI 2017

作者： Chen, Xiaoguang Yang, Xuan Wang, Maosen Zou, Jiancheng Institute of Image Processing and Pattern Recognition North China University of Technology No.5 Jinyuanzhuang Road Shijingshan District Beijing China School of Compute Science and Techonology Beijing University of Posts and Telecommunications No.10 Xitucheng Road Haidian District Beijing China

ISBN: (纸本)9781509048977

Facial expression recognition is a hot research direction of pattern recognition and computer vision. It has been increasingly used in artificial intelligence, human-computer interaction and security monitoring in recent years. Convolution neural network (CNN) as a depth learning architecture can extract the essential features of the image, and in the case of large changes in shooting conditions, its effect is better than the traditional methods of Support Vector Machines(SVM) and Principal Component Analysis(PCA). Therefore, an improved method of facial expression recognition based on CNN is proposed in this paper. The purpose is to classify each facial image as one of the seven facial expressions considered in this study. According to the characteristics of facial expression recognition, a new convolution neural network structure is designed which uses convolution kernel to extract implicit features and max pooling to reduce the dimensions of the extracted implicit features. In comparison to AlexNet network, we can improve the recognition accuracy about 4% higher on the CK+ facial expression database by the aid of Batch Normalization (BN) layer to our network. A facial expression recognition system is constructed in this paper for the convenience of application, and the experimental results show that the system could reach the real-Time needs. © 2017 IEEE.

关键词： Principal component analysis

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A comprehensive survey of oracle character recognition: Challenges, datasets, methodology, and beyond

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pattern recognition 2025年

作者： Jing Li Xueke Chi Qiufeng Wang Kaizhu Huang Da-Han Wang Yongge Liu Cheng-Lin Liu College of Science & Technology Ningbo University Ningbo China School of Advanced Technology Xi’an Jiaotong-Liverpool University Suzhou China Duke Kunshan University Suzhou China Fujian Key Laboratory of Pattern Recognition and Image Understanding School of Computer and Information Engineering Xiamen University of Technology Xiamen China Anyang Normal University Anyang China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China

Oracle character recognition—an analysis of ancient Chinese inscriptions found on oracle bones—has become a pivotal field intersecting archaeology, paleography, and historical cultural studies. Traditional methods of oracle character recognition have relied heavily on manual interpretation by experts, which is not only labor-intensive but also limits broader accessibility to the general public. With recent breakthroughs in pattern recognition and deep learning, there is a growing movement towards the automation of oracle character recognition (OrCR), showing considerable promise in tackling the challenges inherent to these ancient scripts. However, a comprehensive understanding of OrCR still remains elusive. Therefore, this paper presents a systematic and structured survey of the current landscape of OrCR research. We commence by identifying and analyzing the key challenges of OrCR. Then, we provide an overview of the primary benchmark datasets and digital resources available for OrCR. A review of contemporary research methodologies follows, in which their respective efficacies, limitations, and applicability to the complex nature of oracle characters are critically highlighted and examined. Additionally, our review extends to ancillary tasks associated with OrCR across diverse disciplines, providing a broad-spectrum analysis of its applications. We conclude with a forward-looking perspective, proposing potential avenues for future investigations that could yield significant advancements in the field.

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