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Intelligent Computing, Networked Control, and Their Engineering Applications 1st ed. 2017

丛书名： Communications in Computer and Information Science

2017年

作者： Dong Yue Chen Peng Dajun Du Tengfei Zhang Min Zheng Qinglong Han

ISBN: (数字)9789811063732

ISBN: (纸本)9789811063725

The three-volume set CCIS 761, CCIS 762, and CCIS 763 constitutes the thoroughly refereed proceedings of the International Conference on Life System Modeling and Simulation, LSMS 2017, and of the International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2017, held in Nanjing, China, in September 2017. The 208 revised full papers presented were carefully reviewed and selected from over 625 submissions. The papers of this volume are organized in topical sections on: Biomedical signal processing; Computational methods in Organism Modeling; Medical Apparatus and Clinical Applications; Bionics Control methods, Algorithms and Apparatus; Modeling and Simulation of Life Systems; Data Driven Analysis; image and Video processing; Advanced Fuzzy and neural Network Theory and Algorithms; Advanced Evolutionary methods and Applications; Advanced Machine Learning methods and Applications; Intelligent Modeling, Monitoring, and Control of Complex Nonlinear Systems; Advanced methods for Networked Systems; Control and Analysis of Transportation Systems; Advanced Sliding Mode Control and Applications; Advanced Analysis of New Materials and Devices; Computational Intelligence in Utilization of Clean and Renewable Energy Resources; Intelligent methods for Energy Saving and Pollution Reduction; Intelligent methods in Developing Electric Vehicles, Engines and Equipment; Intelligent Computing and Control in Power Systems; Modeling, Simulation and Control in Smart Grid and Microgrid; Optimization methods; Computational methods for Sustainable Environment.

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MAP Tomographic Reconstruction with a Spatially Adaptive Hierarchical image Model

MAP Tomographic Reconstruction with a Spatially Adaptive Hie...

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European signal processing Conference

作者： Christophoros Nikou Department of Computer Science and Engineering University of Ioannina Greece

A method for penalized likelihood tomographic reconstruction is presented which is based on a spatially adaptive stochastic image model. The model imposes onto the image a smoothing Gaussian prior whose parameters follow a Gamma distribution. Three variations of the model are examined: (i) a stationary model, where the Gamma distribution has the same constant parameter for the entire image, (ii) a non stationary model, where this parameter varies with respect to location and (iii) a non stationary directional model where the parameter varies also with respect to orientation (horizontal or vertical direction). In all cases, the MAP criterion provides a closed form solution for both the unknown image and the parameters of the model. Numerical experiments showed that the reconstructions obtained using the proposed image priors outperform the state of the art EM based methods.

关键词： Adaptation models image reconstruction Computational modeling Tomography Mathematical model Europe signal processing

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DEEP DISCRIMINATIVE MANIFOLD LEARNING 41

DEEP DISCRIMINATIVE MANIFOLD LEARNING

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41st IEEE International Conference on Acoustics, Speech and signal processing (ICASSP)

作者： Chien, Jen-Tzung Chen, Ching-Huai Natl Chiao Tung Univ Dept Elect & Comp Engn Hsinchu 30010 Taiwan

ISBN: (纸本)9781479999880

This paper presents a new non-linear dimensionality reduction with stochastic neighbor embedding. A deep neural network is developed for discriminative manifold learning where the class information in transformed low-dimensional space is preserved. Importantly, the objective function for deep manifold learning is formed as the Kullback-Leibler divergence between the probability measures of the labeled samples in high-dimensional and low-dimensional spaces. Different from conventional methods, the derived objective does not require the empirically-tuned parameter. This objective is optimized to attractive those samples from the same class to be close together and simultaneously impose those samples from different classes to be far apart. In the experiments on image and audio tasks, we illustrate the effectiveness of the proposed discriminative manifold learning in terms of visualization and classification performance.

关键词： Manifold learning deep neural network discriminative learning pattern classification

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Hyperspectral image Classification with Markov Random Fields and a Convolutional neural Network

arXiv

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arXiv 2017年

作者： Cao, Xiangyong Zhou, Feng Xu, Lin Meng, Deyu Xu, Zongben Paisley, John IEEE IEEE School of Mathematics and Statistics Xi’an Jiaotong University Xi’an710049 China National Laboratory of Radar Signal Processing Xidian University Xi’an China NYU Multimedia and Visual Computing Lab New York University Abu Dhabi United Arab Emirates Department of Electrical Engineering & Data Science Institute Columbia University New YorkNY United States Columbia University China Scholarship Council

This paper presents a new supervised classification algorithm for remotely sensed hyperspectral image (HSI) which integrates spectral and spatial information in a unified Bayesian framework. First, we formulate the HSI classification problem from a Bayesian perspective. Then, we adopt a convolutional neural network (CNN) to learn the posterior class distributions using a patch-wise training strategy to better use the spatial information. Next, spatial information is further considered by placing a spatial smoothness prior on the labels. Finally, we iteratively update the CNN parameters using stochastic gradient decent (SGD) and update the class labels of all pixel vectors using α-expansion min-cut-based algorithm. Compared with other state-of-the-art methods, the proposed classification method achieves better performance on one synthetic dataset and two benchmark HSI datasets in a number of experimental settings. Copyright © 2017, The Authors. All rights reserved.

关键词： Spectroscopy

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DEEP STACKING NETWORK WITH COARSE FEATURES FOR HYPERSPECTRAL image CLASSIFICATION 8

DEEP STACKING NETWORK WITH COARSE FEATURES FOR HYPERSPECTRAL...

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8th Workshop on Hyperspectral image and signal processing - Evolution in Remote Sensing (WHISPERS)

作者： He, Mingyi Li, Xiaohui Northwestern Polytech Univ Earth Observat Ctr Sch Elect & Informat Shaanxi Key Lab Informat Acquisit & Proc Xian 710129 Shaanxi Peoples R China

ISBN: (纸本)9781509006083

Hyperspectral image (HSI) classification attracts increasing attentions in remote sensing community for its academic significance and potential wide applications. Although most of used existing "intelligent" methods extracting the features of original hyperspectral data are based on shallow-layer networks such as neural network (NN) and support vector machine (SVM) for its simplicity in realization, two deep neural networks (DNN), i.e. the deep convolutional network (DCN) and the deep belief network (DBN), have been used for HSI classifications with improved performance, as deep learning recently achieves great success for its ability in deep feature extraction and representations. In this paper, another DNN, a deep stacking network (DSN) based approach with coarse features for hyperspectral image classification is proposed, as its advantages over shadow networks and other deep models in its simplicity and batch-mode learning - not requiring stochastic gradient descent that other DNNs require. In this approach, the inputs to the DSN are coarse features, i.e. coarse spectral features by band reduction, coarse spatial features extracted by PCA from original HSI, or combination of both. The fine deeper features in the hyperspectral data are gradually obtained by employing the nonlinear activation function on the hidden layer nodes of each module, which is different from the current DSN that usually uses linear weights between hidden layer to output layer. A closed limit on its node's input - output is also exerted. The theoretical analysis and experimental results with AVIRIS hyperspectral image have shown that, (1) the proposed DSN approach achieves improved classification performance compared with shadow networks, (2) the new DSN approach with both the coarse spectral feature and coarse spatial feature has higher classification accuracy compared to that with either only, (3) this study has insight of necessity to develop new deep neural network for HSI classification

关键词： deep stacking network hyperspectral image logistic regression classification

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Extracting Region of Interest for Palmprint by Convolutional neural Networks 6

Extracting Region of Interest for Palmprint by Convolutional...

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6th International Conference on image processing Theory, Tools and Applications (IPTA)

作者： Bao, Xianjie Guo, Zhenhua Tsinghua Univ Grad Sch Shenzhen Shenzhen Peoples R China

ISBN: (纸本)9781467389105

Palm ROI extraction is one of the most important processes in palmprint recognition. The core idea is to employ the valley points between the fingers to establish a coordinate system and then obtain the ROI of palmprints. However when extracting the keypoints, conventional methods have three problems: (i) they are so sensitive to parameters and background noise due to lack of joint optimization, (ii) accuracy of the location of keypoints is not good enough, (iii) extracting speed can be faster. To address the above problems, this paper presents a novel approach to extract palmprint ROI using convolutional neural net. First, we present a new CNN to identify the palmprint being a left or right hand. Then we propose a specific designed and optimized CNN to detect the keypoints. Finally we test our method using palmprint verification algorithm, competitive coding. Experimental results show that the proposed novel method is not only fast and efficient, but also robust for ROI extraction.

关键词： Palmprint recognition Convolutional neural net Region of interest Keypoint detection

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Biometric spoofing detection by a Domain-aware Convolutional neural Network 12

Biometric spoofing detection by a Domain-aware Convolutional...

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12th International Conference on signal-image Technology and Internet-Based Systems (SITIS)

作者： Gragnaniello, Diego Poggi, Giovanni Sansone, Carlo Verdoliva, Luisa CNR Natl Res Council Italy ICAR Inst High Performance Comp & Networking Naples Italy Univ Federico Naples II DIETI Naples Italy

ISBN: (纸本)9781509056989

Biometric authentication systems are pervasive in modern society, but they are quite vulnerable to spoofing attacks. Research on spoofing (or liveness) detection is therefore very active. A number of methods have been proposed in the literature, sometimes with very promising results, but limited robustness with respect to the large variety of biometric traits, sensors, and attacks encountered in real-life. Recently, methods based on Convolutional neural Networks (CNNs) are drawing great attention, given their success in many other image processing tasks. However, despite some promising results, they seem to suffer the same robustness problem, requiring heavy training to work properly. Here, we propose a new CNN architecture for biometric spoofing detection. Thanks to domain-specific knowledge, accounted for through a suitable loss function, a compact architecture is obtained, allowing reliable training also in the presence of small-size datasets. Experiments prove the proposal to provide state-of-art performance on several widespread datasets for face and iris liveness detection.

关键词： Biometric spoofing liveness detection convolutional neural networks

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Classification of Cervical Cancer using Artificial neural Networks 12

Classification of Cervical Cancer using Artificial Neural Ne...

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12th International Conference on Communication Networks (ICCN) / 12th International Conference on Data Mining and Warehousing (ICDMW) / 12th International Conference on image and signal processing (ICISP)

作者： Devi, M. Anousouya Ravi, S. Vaishnavi, J. Punitha, S. Pondicherry Univ Sch Engn & Technol Pondicherry India

Artificial neural network (ANN) plays an important role in many medical imaging applications. The detection of cervical cancer cells uses an ANN for classifying the normal and abnormal cells in the cervix region of the uterus. Cervical cancer detection is very challenging because this cancer occurs without any symptoms. The classification between the normal, abnormal and cancerous cells is identified by using an artificial neural network which produces accurate results than the manual screening methods like Pap smear and Liquid cytology based (LCB) test. The ANN uses several architectures for easy and accurate detection of cervical cells. In this paper, a survey and analysis on the different types of architecture in the ANN with its accuracy results and performance are discussed. A brief description about the working and detection of cervical cancer is presented which is useful for the classification of normal and abnormal cervical cells. (C) 2016 The Authors. Published by Elsevier B.V.

关键词： Artificial neural Network (ANN) Cervical Cancer Liquid Cytology Based Test (LCB) Pap Smear Test

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image Fusion and Super-Resolution with Convolutional neural Network

Image Fusion and Super-Resolution with Convolutional Neural ...

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7th Chinese Conference on Pattern Recognition (CCPR)

作者： Zhong, Jinying Yang, Bin Li, Yuehua Zhong, Fei Chen, Zhongze Univ South China Sch Elect Engn Hengyang 421001 Peoples R China

ISBN: (纸本)9789811030055;9789811030048

image fusion aims to integrate multiple images of the same scene into an artificial image which contains more useful information than any individual one. Due to the constraints of imaging sensors and signal transmission broadband, the resolution of most source images is limited. In traditional processing framework, super-resolution is conducted to improve the resolution of the source images before the fusion operations. However, those super-resolution methods do not make full use of the multi-resolution characteristics of images. In this paper, a novel jointed image fusion and super-resolution algorithm is proposed. Source images are decomposed into undecimated wavelet (UWT) coefficients, the resolution of which is enhanced with convolutional neural network. Then, the coefficients are further integrated with certain fusion rule. Finally, the fused image is constructed from the combined coefficients. The proposed method is tested on multi-focus images, medical images and visible light and near infrared ray images respectively. The experimental results demonstrate the superior performances of the proposed method.

关键词： image fusion Super-resolution Convolutional neural network

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Integration of Unsupervised and Supervised Criteria for Deep neural Networks Training 25th

Integration of Unsupervised and Supervised Criteria for Deep...

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25th International Conference on Artificial neural Networks (ICANN)

作者： Zamora-Martinez, Francisco Munoz-Almaraz, Javier Pardo, Juan Univ CEU Cardenal Herrera Dept Ciencias Fis Matemat & Comp Valencia 46115 Spain

ISBN: (纸本)9783319447810;9783319447803

Training Deep neural Networks has been a difficult task for a long time. Recently diverse approaches have been presented to tackle these difficulties, showing that deep models improve the performance of shallow ones in some areas like signal processing, signal classification or signal segmentation, whatever type of signals, e.g. video, audio or images. One of the most important methods is greedy layer-wise unsupervised pre-training followed by a fine-tuning phase. Despite the advantages of this procedure, it does not fit some scenarios where real time learning is needed, as for adaptation of some time-series models. This paper proposes to couple both phases into one, modifying the loss function to mix together the unsupervised and supervised parts. Benchmark experiments with MNIST database prove the viability of the idea for simple image tasks, and experiments with time-series forecasting encourage the incorporation of this idea into on-line learning approaches. The interest of this method in time-series forecasting is motivated by the study of predictive models for domotic houses with intelligent control systems.

关键词： Deep learning Stacked auto-encoders Supervised learning Time-series forecasting

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