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Iterative data adaptive anisotropic image filtering

Iterative data adaptive anisotropic image filtering

引用

2012 International Conference on Machine Learning and Cybernetics, ICMLC 2012

作者： Yang, Chang-Cai Zheng, Xin-Yi Tian, Jin-Wen Shang, Ke Tian, Xin Hao, Wei Institute for Pattern Recognition and Artificial Intelligence Science and Technology on Multi-Spectral Information Processing Laboratory Huazhong University of Science and Technology Wuhan 430074 China Electrical Engineering and Renewable Energy School China Three Gorges University Yichang 443002 China Institute of Intelligent Vision and Image Information College of Computer and Information Technology China Three Gorges University Yichang 443002 China

ISBN: (纸本)9781467314855

This paper presents a novel data-adaptive anisotropic filtering technique built on top of an iterative scheme. This new technique can preserve the original significant structures while suppressing noises to the largest extent. To achieve this goal, dominant orientation information of all gradients is used to control the local kernel adaptively. This results in elongated and elliptical contours spread along the directions of the local edge structure. With these locally adapted kernels, the smoothing is mostly effective along the edges, rather than across them. Therefore details in the original input can be preserved. The performance of the proposed data-adaptive anisotropic filter together with its iterative scheme are compared with the conventional Gaussian filter, anisotropic diffusion filter, the structure-adaptive anisotropic filter, and the iterative kernel regression scheme. The experimental results on both simulated Gaussian and Poission noisy images indicate that the proposed method performs the best, and the iterative scheme can significantly improve the performance of the noise suppressing while delivering high computation efficiency. © 2012 IEEE.

关键词： Iterative methods

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One-Class SVM assisted accurate tracking

One-Class SVM assisted accurate tracking

引用

ACM/IEEE International Conference on Distributed Smart Cameras, ICDSC

作者： Keren Fu Chen Gong Yu Qiao Jie Yang Irene Guy Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai China Chalmers tekniska hogskola Goteborg SE

Recently, tracking is regarded as a binary classification problem by discriminative tracking methods. However, such binary classification may not fully handle the outliers, which may cause drifting. In this paper, we argue that tracking may be regarded as one-class problem, which avoids gathering limited negative samples for background description. Inspired by the fact the positive feature space generated by One-Class SVM is bounded by a closed sphere, we propose a novel tracking method utilizing One-Class SVMs that adopt HOG and 2bit-BP as features, called One-Class SVM Tracker (OCST). Simultaneously an efficient initialization and online updating scheme is also proposed. Extensive experimental results prove that OCST outperforms some state-of-the-art discriminative tracking methods on providing accurate tracking and alleviating serious drifting.

关键词： Target tracking Support vector machines Training Feature extraction Face Boosting Semisupervised learning

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Integrating Segmentation and Classification Accuracy for Accuracy Assessment in Object-based image Analysis

Integrating Segmentation and Classification Accuracy for Acc...

引用

International Conference on Audio, Language and image Processing

作者： Nan Li Hong Huo Tao Fang Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education Institute of Image Processing and PatternRecognition Department of Automation Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education

ISBN: (纸本)9781467301732

An accuracy assessment method that integrates segmentation and classification accuracy is proposed to meet the requirements of object-based image analysis. Segmentation errors are measured by establishing the relationship between pixels and their corresponding segments according to the overlaps of segments and reference polygons. Then, two improved confusion matrices that take the segmentation errors into consideration are used: one for pixel-level classification results, and the other for object-level classification results. A final accuracy assessment combines the statistics of these two confusion matrices. The proposed method can be applied to segmentation scale selection in the hierarchical interpretation system. An experiment on a SPOT5 image demonstrates the effectiveness of this method for segmentation scale selection, which can guide the fusion of objects of different scales to obtain a higher accuracy.

关键词： segmentation image analysis Segmentation object orientation confusion matrix pixel

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Epileptic EEG Signal Classification with ANFIS Based on Harmony Search Method

Epileptic EEG Signal Classification with ANFIS Based on Harm...

引用

International Conference on Computational Intelligence and Security

作者： Jing Wang X.Z. Gao Jarno M.A. Tanskanen Ping Guo Laboratory of Image Processing and Pattern Recognition Beijing Normal University Beijing China Department of Automation and Systems Technology Aalto University School of Electrical Engineering Finland Tampereen yliopisto - Hervannan kampus Tampere Pirkanmaa FI

In this paper, the Adaptive Neuro-Fuzzy Inference System (ANFIS) is used for the classification of the epileptic electroencephalogram (EEG) signals. The ANFIS combines the adaptation capability of the neural networks and the fuzzy logic-based qualitative approach together. A given input/output data set is deployed to construct a fuzzy inference system, whose membership function parameters are trained using a back propagation algorithm in combination with a least squares method. However, the training method sometimes may lead to local optima. We here propose a new strategy of hybrid training algorithm based on the fusion of the ANFIS and Harmony Search (HS), HS-ANFIS, which is adopted to tune all the parameters of the ANFIS. The validity of our method is verified by numerical experiments.

关键词： Electroencephalography Epilepsy Feature extraction Training pattern classification Adaptive systems Expert systems

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Experimental Comparison of Geometric, Arithmetic and Harmonic Means for EEG Event Related Potential Detection

Experimental Comparison of Geometric, Arithmetic and Harmoni...

引用

International Conference on Computational Intelligence and Security

作者： Jarno M.A. Tanskanen X.Z. Gao Jing Wang Ping Guo Jari A.K. Hyttinen Vassil S. Dimitrov Department of Biomedical Engineering Tampere University of Technology and BioMediTech Finland College of Information Engineering Shanghai Maritime University China Laboratory of Image Processing and Pattern Recognition Beijing Normal University Beijing China ATIPS Laboratory University of Calgary AB Canada

In this paper, we experimentally evaluate three different averaging methods for processing of electroencephalogram (EEG) event related potentials (ERPs) measured from scalp in response to repeated stimulus. In ERP applications, arithmetic mean (AM) is normally employed in processing the ERPs prior to ERP detection, whereas also other averaging methods might have beneficial properties. Fast ERP detection is essential, for example, in brain computer interfaces and during spine surgery. Thus, it is of interest to search for methods to aid in detecting ERPs with as few stimulus repetitions as possible. Here, noise reduction properties of AM, geometric mean (GM), and harmonic mean (HM) are demonstrated with simulations, and ERP processing by the three methods is illustrated by processing real visual evoked potentials (VEPs).

关键词： Electroencephalography Gaussian noise Educational institutions Electrodes Visualization Harmonic analysis

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Software fault prediction framework based on aiNet algorithm

Software fault prediction framework based on aiNet algorithm

引用

2011 7th International Conference on Computational Intelligence and Security, CIS 2011

作者： Yin, Qian Luo, Ruiyi Guo, Ping Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China

ISBN: (纸本)9780769545844

Software fault prediction techniques are helpful in developing dependable software. In this paper, we proposed a novel framework that integrates testing and prediction process for unit testing prediction. Because high fault prone metrical data are much scattered and multi-centers can represent the whole dataset better, we used artificial immune network (aiNet) algorithm to extract and simplify data from the modules that have been tested, then generated multi-centers for each network by Hierarchical Clustering. The proposed framework acquires information along with the testing process timely and adjusts the network generated by aiNet algorithm dynamically. Experimental results show that higher accuracy can be obtained by using the proposed framework. © 2011 IEEE.

关键词： Forecasting

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Handwritten and Machine Printed Text Separation in Document images Using the Bag of Visual Words Paradigm

Handwritten and Machine Printed Text Separation in Document ...

引用

International Workshop on Frontiers in Handwriting recognition

作者： Konstantinos Zagoris Ioannis Pratikakis Apostolos Antonacopoulos Basilis Gatos Nikos Papamarkos Pattern Recognition and Image Analysis (PRImA) Research Laboratory School of Computing Science and Engineering University of Salford Greater Manchester UK Department of Electrical and Computer Engineering Democritus University of Thrace Xanthi Greece Institute of Informatics and Telecommunications National Center for Scientific Research Demokritos Athens Greece

In a number of types of documents, ranging from forms to archive documents and books with annotations, machine printed and handwritten text may be present in the same document image, giving rise to significant issues within a digitisation and recognition pipeline. It is therefore necessary to separate the two types of text before applying different recognition methodologies to each. In this paper, a new approach is proposed which strives towards identifying and separating handwritten from machine printed text using the Bag of Visual Words paradigm (BoVW). Initially, blocks of interest are detected in the document image. For each block, a descriptor is calculated based on the BoVW. The final characterization of the blocks as Handwritten, Machine Printed or Noise is made by a Support Vector Machine classifier. The promising performance of the proposed approach is shown by using a consistent evaluation methodology which couples meaningful measures along with a new dataset.

关键词： Visualization Noise image segmentation Hidden Markov models Gabor filters Handwriting recognition Text recognition

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Sparse representation for multi-label image annotation

Sparse representation for multi-label image annotation

引用

2011 7th International Conference on Computational Intelligence and Security, CIS 2011

作者： Xu, Bingxin Guo, Ping Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing 100875 China

ISBN: (纸本)9780769545844

image annotation is the process of assigning proper keywords to describe the content of a given image, which can be regarded as a problem of multi-object image classification. In this paper, a general multi-label annotation algorithm is proposed, which is based on sparse representation theory and employs a multi-level decision method to deal with the multi-object classification problem. The experimental results show that the proposed algorithm can provide more promising results compared with the traditional classification based image annotation methods. © 2011 IEEE.

关键词： image analysis

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Multi-level kernel machine for scene image classification

Multi-level kernel machine for scene image classification

引用

2011 7th International Conference on Computational Intelligence and Security, CIS 2011

作者： Hu, Junlin Guo, Ping Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing 100875 China

ISBN: (纸本)9780769545844

Recently, a new representation for recognizing instances and categories of scenes called spatial Principal component analysis of Census Transform histograms (PACT) has shown its excellent performance in the scene image classification task. PACT captures local structures of an image through the Census Transform (CT), meanwhile, large scale structures are captured by the strong correlation between neighboring CT values and the histogram. However, the original spatial PACT only simply concatenates all levels compact histograms together, and discards the difference between various levels. In order to improve this problem, we propose a multi-level kernel machine method, which computes a set of base kernels at each level of pyramid of PACT, and finds optimal weights for best fusing all these base kernels for scene recognition. Experiments on two popular benchmark datasets demonstrate that our proposed multi-level kernel machine method outperforms the spatial PACT on scene recognition. Besides, our method is easy to be implemented comparing with spatial PACT. © 2011 IEEE.

关键词： Principal component analysis

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Epileptic EEG signal classification with marching pursuit based on harmony search method

Epileptic EEG signal classification with marching pursuit ba...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Ping Guo Jing Wang X. Z. Gao Jarno M. A. Tanskanen Laboratory of Image Processing and Pattern Recognition Beijing Normal University Beijing China School of Foundational Education Peking University Health Science Center China Department of Automation and Systems Technology Aalto University School of Electrical Engineering Finland Department of Biomedical Engineering Tampere University of Technology Finland

In Epilepsy EEG signal classification, the main time-frequency features can be extracted by using sparse representation with marching pursuit (MP) algorithm. However, the computational burden is so heavy that it is almost impossible to apply MP to real time signal processing. To reduce complexity of sparse representation, we propose to adopt harmony search method in searching the best atoms. Because harmony search method can find the best atoms in continuous time-frequency dictionary, the performance of epilepsy EEG signal classification is enhanced. The validity of this method is proved by experimental results.

关键词： Feature extraction Electroencephalography Dictionaries Classification algorithms Training Matching pursuit algorithms Accuracy

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