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International Symposium on Biomedical Imaging

作者： Shengran Su Zhifan Gao Heye Zhang Qiang Lin William Kongto Hau Shuo Li College of Sciences Zhejiang University of Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences LiKaShing Faculty of Medicine University of Hong Kong University of Western Ontario

ISBN: (纸本)9781509011735

This paper describes an artificial neural network (ANN) method that employs a feature-learning algorithm to detect the lumen and MA borders in intravascular ultrasound (IVUS) images. Three types of imaging features including spatial, neighboring, and gradient features were used as the input features to the neural network, and then the different vascular layers were distinguished using two sparse autoencoders and one softmax classifier. To smooth the lumen and MA borders detected by the ANN method, we used the active contour model. The performance of our approach was compared with the manual drawing method and another existing method on 538 IVUS images from six subjects. Results showed that our approach had a high correlation (r= 0.9284~ 0.9875 for all measurements) and good agreement (bias= 0.0148~ 0.4209 mm) with the manual drawing method, and small detection error (lumen border: 0.0928 + 0.0935 mm, MA border: 0.1056+0.1088 mm). The average time to process each image was 14 + 4.6 seconds. The obtained results indicate that our proposed approach can be used to efficiently and accurately detect the lumen and MA borders in IVUS images.

关键词： Intravascular image Deep neural network Image segmentation sparse autoencoder

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SD-MSAEs: Promoter recognition in human genome based on deep feature extraction

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JOURNAL OF BIOMEDICAL INFORMATICS 2016年 61卷 55-62页

作者： Xu, Wenxuan Zhang, Li Lu, Yaping Soochow Univ Sch Comp Sci & Technol Suzhou 215006 Jiangsu Peoples R China Soochow Univ Joint Int Res Lab Machine Learning & Neuromorph Suzhou 215006 Jiangsu Peoples R China Collaborat Innovat Ctr Novel Software Technol & I Nanjing 210000 Jiangsu Peoples R China

The prediction and recognition of promoter in human genome play an important role in DNA sequence analysis. Entropy, in Shannon sense, of information theory is a multiple utility in bioinformatic details analysis. The relative entropy estimator methods based on statistical divergence (SD) are used to extract meaningful features to distinguish different regions of DNA sequences. In this paper, we choose context feature and use a set of methods of SD to select the most effective n-mers distinguishing promoter regions from other DNA regions in human genome. Extracted from the total possible combinations of n-mers, we can get four sparse distributions based on promoter and non-promoters training samples. The informative n-mers are selected by optimizing the differentiating extents of these distributions. Specially, we combine the advantage of statistical divergence and multiple sparse auto-encoders (MSAEs) in deep learning to extract deep feature for promoter recognition. And then we apply multiple SVMs and a decision model to construct a human promoter recognition method called SD-MSAE5. Framework is flexible that it can integrate new feature extraction or new classification models freely. Experimental results show that our method has high sensitivity and specificity. (C) 2016 Elsevier Inc. All rights reserved.

关键词： Context features Promoter recognition sparse autoencoder Statistical divergence Support vector machine

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High-speed Railway Clearance Surveillance System Based on Convolutional Neural Networks 8

High-speed Railway Clearance Surveillance System Based on Co...

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8th International Conference on Digital Image Processing (ICDIP)

作者： Wang, Yang Yu, Zujun Zhu, Liqiang Guo, Baoqing Beijing Jiaotong Univ Sch Mech Elect & Control Engn Beijing 100044 Peoples R China

ISBN: (纸本)9781510605039;9781510605046

In this paper, the convolutional neural networks with the pre-trained kernels are applied to the video surveillance system, which has been built along the Shanghai-Hangzhou high-speed railway to monitor the railway clearance scene and will output the alarm images with the dangerous intruding objects in. The video surveillance system will firstly generate the images which are suspected of containing the dangerous objects intruding the clearance. The convolutional neural networks with the pre-trained kernels are applied to process these suspicious images to eliminating the false alarm images, only contain the trains and the empty clearance scene, from other suspicious images before the final output. Experimental result shows that, the process of each test image only takes 0.16 second and has a high accuracy.

关键词： High-speed railway train recognition sparse autoencoder pre-trained kernels convolutional neural networks

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Deep Learning-Based Recognition of Underwater Target

Deep Learning-Based Recognition of Underwater Target

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IEEE International Conference on Digital Signal Processing (DSP)

作者： Cao, Xu Zhang, Xiaomin Yu, Yang Niu, Letian Northwestern Polytech Univ Sch Marine Sci & Technol Xian 710072 Peoples R China

ISBN: (纸本)9781509041657

Underwater target recognition remains a challenging task due to the complex and changeable environment. There have been a huge number of methods to deal with this problem. However, most of them fail to hierarchically extract deep features. In this paper, a novel deep learning framework for underwater target classification is proposed. First, instead of extracting features relying on expert knowledge, sparse autoencoder (AE) is utilized to learn invariant features from the spectral data of underwater targets. Second, stacked autoencoder (SAE) is used to get high-level features as a deep learning method. At last, the joint of SAE and softmax is proposed to classify the underwater targets. Experiment results with the received signal data from three different targets on the sea indicated that the proposed approach can get the highest classification accuracy compared with support vector machine (SVM) and probabilistic neural network (PNN).

关键词： Deep learning Underwater target recognition sparse autoencoder Stacked autoencoder

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Automatic Recognition of ISAR Images Based on Deep Learning

Automatic Recognition of ISAR Images Based on Deep Learning

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CIE International Conference on Radar (RADAR)

作者： He, Xingyu Tong, Ningning Hu, Xiaowei Air Force Engn Univ Air & Missile Def Coll Xian Peoples R China

ISBN: (纸本)9781509048281

The problem of target classification of non-cooperative airplane using inverse synthetic aperture radar (ISAR) images is studied. ISAR image's variation with the radar imaging view makes the classification difficult. The point scatter models of five different airplanes are built. Deep learning methods have been shown to be able to classify images in a variety of disciplines with a high level of accuracy. As an effective deep learning algorithm, sparse autoencoder (SA) learning algorithm can automatically learn features from unlabeled data. In this paper, the SA algorithm is used to extract the features of the ISAR images. The extracted features are served as the input of the softmax classifier and then the recognition and classification of ISAR images are achieved. Simulation results verify the effectiveness and superiority of the proposed method.

关键词： ISAR images deep learning recognition sparse autoencoder softmax

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Hand gesture recognition using neural network based techniques 13

Hand gesture recognition using neural network based techniqu...

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13th Symposium on Neural Networks and Applications (NEUREL)

作者： Bobic, Vladislava Tadic, Predrag Kvascev, Goran Univ Belgrade Sch Elect Engn Bul Kralja Aleksandra 73 Belgrade 11120 Serbia

ISBN: (纸本)9781509015306

In this paper, two neural network based methods were implemented for recognition of images showing 10 hand gestures. Images were available from 24 subjects and captured on two different backgrounds and with several space orientations. Firstly, Histogram of Oriented Gradients method was applied for feature extraction and training was performed with multilayer feedforward neural network with backpropagation algorithm. Within the second method, sparse autoencoder with 5 hidden layers and decreasing number of neurons was implemented. For both methods it was examined how number of descriptors influences the accuracy of classification and found relationship was used to determine best performing case. Both classification methods achieved accuracy of about 92.5%, by using the similar number of estimated parameters.

关键词： Hand gesture recognition Histogram of Oriented Gradients sparse autoencoder

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Deep Learning-Based Classification of Massive Electrocardiography Data

Deep Learning-Based Classification of Massive Electrocardiog...

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2016 IEEE Advanced Information Management,Communicates,Electronic and Automation Control Conference(IMCEC 2016)

作者： Lin Zhou Yan Yan Xingbin Qin Chan Yuan Dashun Que Lei Wang Information and Communication Engineering WuHan University of Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences

ISBN: (纸本)9781509001668

Classification is the basis of electrocardiography(ECG) *** the last decades,a large number of methods were proposed to deal with the classification of ECG *** this paper a kind of deep learning method is introduced into ECG beats *** create a classifier with stacked sparse autoencoder(SAE),and then combine the softmax regression with the SAE networks to consummate the classifier,from which we can get higher accuracy in the classification *** the deep networks architecture,we use the stacked sparse autoencoder to get high-level *** results with the MIT-BIH ECG dataset confirmed that classifier build in this autoencoder based deep networks method perform better in the classical classification problem.

关键词： electrocardiography beats classification sparse autoencoder softmax regression deep learning

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Machine Learning Anomaly Detection in Large Systems 52

Machine Learning Anomaly Detection in Large Systems

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IEEE AUTOTESTCON

作者： Murphree, Jerry DRS Technol Huntsville AL 35808 USA

ISBN: (纸本)9781509007899

We have a need for methods to efficiently determine the health of a system. Diagnostics and prognostics determine system heath through analysis of data from sensors. Anomalies in the data can help us determine if there is a failure or a pending failure. There are common statistical methods to detect anomalies in individual measurements. For systems with many measurements, the anomalies may occur as specific combinations of values. Large systems have various associated states and modes which define the valid measurements. The amount of data to analyze grows very quickly as the system becomes more complex. In recent years techniques have been developed to address large data analysis. Machine Learning encompasses a broad selection of tools to optimize a statistical model of the data. These tools include supervised learning techniques, such as linear regression and logistic regression, in which training data exists to tune the model. Unsupervised learning, such as clustering, is used to explore data which does not have a defined output label associated with inputs data. Standard approaches to training supervised learning systems require a large sample of positive and negative outcome data. Some uses of machine learning involve data where there are very few cases of negative outcomes. There are machine learning algorithms defined as Anomaly Detection which are designed to deal with this type of data. Simple algorithms include Gaussian Distribution Analysis, which assumes independence in distributions of data. Large Systems with anomalies defined in the dependent combinations of data require either a manual creation of combinations of independent variables, or Multivariate Gaussian Distribution Analysis, which does not scale well for large systems. A further complication is the mixture of linear and discrete data. Neural Networks are a type of learning system which has been applied to each of the individual needs addressed above. This paper describes an approach to anomal

关键词： Machine Learning Anomaly Detection Large Systems sparse autoencoder Neural Networks

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Effects of deep neural network parameters on classification of bearing faults 42

Effects of deep neural network parameters on classification ...

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42nd Conference of the Industrial Electronics Society, IECON 2016

作者： Ahmed, H.O.A. Dennis Wong, M.L. Nandi, A.K. Department of Electronic and Computer Engineering Brunel University London UxbridgeUB8 3PH United Kingdom Faculty of Engineering Computing and Science Swinburne University of Technology Sarawak Campus Sarawak Malaysia College of Electronic and Information Engineering Tongji University Shanghai China

ISBN: (纸本)9781509034741

Automatic fault detection and classification for roller element bearings is an important issue for rotating machine condition monitoring. In this paper, we classify roller element bearings fault classes under two and three hidden layers' deep neural network framework based on sparse autoencoder. This allows us to learn and extract features for the bearing vibration samples in an unsupervised manner using the encoder part of the autoencoder. Then we form the deep neural network by stacking the encoders in each stage of the hidden layers together with the softmax layer. Classification performance using the full deep network and backpropagation compared, and effects of different deep neural network parameters on the classification accuracy are studied here. © 2016 IEEE.

关键词： Bearing Fault Classification Deep Neural Network (DNN) Machine Condition Monitoring sparse autoencoder

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Vertical Axis Detection for Sport Video Analytics

Vertical Axis Detection for Sport Video Analytics

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International Conference on Digital Image Computing - Techniques and Applications (DICTA)

作者： Zeng, Rui Lakemond, Ruan Denman, Simon Sridharan, Sridha Fookes, Clinton Morgan, Stuart Queensland Univ Technol Speech Audio Image & Video Technol Lab Brisbane Qld Australia Imaginat Technol King Langley England Australian Inst Sport Canberra ACT Australia

ISBN: (纸本)9781509028962

When processing video, it is normally assumed that cameras are vertically oriented such that people appear upright, which helps simplify subsequent processing such as person detection. In real situations, due to the need to provide maximum coverage of the viewing space, cameras are usually placed with arbitrary orientations so the apparent vertical axis of the videos captured may not correspond to the true vertical direction of the captured scene. To rectify this situation, we propose a classification-based system, which normalizes the video compensating for the camera orientation. We demonstrate the performance of the system for outdoor sports video. Our system works as follows: From an arbitrary set of sports videos, we first automatically create a training/testing image dataset, in which players have various orientations. Our classifier is a stacked autoencoder connected to a softmax output layer, which is trained using this dataset for estimating the orientation of players. The orientation of an input video is normalized according to the orientations of player patches, whose angles of orientation are estimated by the above trained classifier. The experiments conducted on hockey field video dataset show that the proposed system is able to estimate the true vertical axis of an input video accurately.

关键词： sparse autoencoder video orientation normalisation human detection

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