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Semantic Background Estimation in Video Sequences

Semantic Background Estimation in Video Sequences

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international conference on signal processing and Integrated Networks (SPIN)

作者： Andreas Savakis Aadeesh Milind Shringarpure Rochester Institute of Technology Rochester New York USA

We present a method that estimates the scene background in videos by utilizing semantic segmentation to extract foreground objects, such as people or cars, and stitching background regions to reconstruct the background. Inspired by recent developments in deep learning, we utilize semantic segmentation based on Conditional Random Field as Recurrent Neural Networks (CRF as RNN) to detect the regions of important objects in each frame and generate a foreground-background map. We use these segmentation maps to extract the background regions from each frame and then stitch them over consecutive frames to obtain the full background for the video sequence. Our foreground/background estimation approach has potential applications in change detection, video surveillance, video compression and video privacy. We illustrate the effectiveness of our method on example videos from the Change Detection (CDNET) dataset.

关键词： Semantics Estimation Video sequences Image segmentation Convolution machine learning

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Deep Neural Network Architecture for Anomaly Based Intrusion Detection System

Deep Neural Network Architecture for Anomaly Based Intrusion...

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international conference on signal processing and Integrated Networks (SPIN)

作者： Sidharth Behera Ayush Pradhan Ratnakar Dash Dept. Of Computer Science and Engineering National Institute of Technology Rourkela India Dept. Of Computer Science and Information Technology National Institute of Technology Rourkela India

this paper presents a deep learning neural network for classifying the network traffic data. In this regard convolutional neural network with rectified linear activation function has been employed. the class score is computed at the final fully connected layer using the dropout mechanism. A k fold cross validation has been carried out to validate the model and the value of k is set to 10. NSL-KDD cup 1999 dataset has been used in the experiment to classify between normal state and 4 different attacks. the accuracy of the suggested model for NSL-KDD cup 199 along with another state of the art techniques is presented to show the effectiveness of the suggested model.

关键词： Neural networks Hidden Markov models Neurons Computer architecture Intrusion detection machine learning Feature extraction

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Automatic Clustering simultaneous Feature Subset Selection using Differential Evolution

Automatic Clustering simultaneous Feature Subset Selection u...

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international conference on signal processing and Integrated Networks (SPIN)

作者： V. Sesha Srinivas A. Srikrishna B. Eswara Reddy JNTUA Ananthapuramu Andhra Pradesh India RVR&JC College of Engineering Guntur Andhra Pradesh India JNTUA College of Engineering Kalikiri Kalikiri Andhra Pradesh India

Clustering is important and widely used in variety of machine learning applications. High dimensionality is a curse to clustering, that declines the algorithm performance in knowledge discovery and increases the algorithm complexity. the high dimensionality risk can be reduced with proper selection of good subset of features by avoiding irrelevant features. Selection of good clusters with proper subset of features is posed as a problem of optimization, can be solved with powerful Meta heuristic methods. Till date, a number of evolutionary based solutions are available for both problems automatic clustering and feature selection. From a decade, Automatic Clustering using Differential evolution is found to be one of the successful methods in automatic clustering considering all features. there is no algorithm that finds optimal clusters with simultaneous feature sub set selection. the paper proposes a novel Automatic Clustering with simultaneous Feature Subset Selection using Differential Evolution (ACFSDE) algorithm. ACFSDE is an enhanced variant to ACDE, defines a new chromosome structure for selection of optimal features and/for optimal clusters. Experiments are conducted in two fold; one is, using numeric UCI benchmark datasets and synthetic data sets. Second is to study the performance of ACFSDE for texture image segmentation by applying on images. the results on numeric data are evaluated using six clustering validity measures and are compared with five other existing clustering algorithms. the ACFSDE results are very prominent with more than 80% of average accuracy.

关键词： Clustering algorithms signal processing algorithms Biological cells Error analysis Evolution (biology) Optimization Feature extraction

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A Novel Method Design for Diagnosis of Psychological Symptoms of Depression Using Speech Analysis 5

A Novel Method Design for Diagnosis of Psychological Symptom...

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5th international conference on Orange Technologies (ICOT)

作者： Lu, Xiaoyong Zhou, Aibao Yang, Hongwu Northwest Normal Univ Sch Phys & Elect Sch Psychol Lanzhou Gansu Peoples R China Northwest Normal Univ Sch Psychol Lanzhou Gansu Peoples R China Northwest Normal Univ Sch Phys & Elect Lanzhou Gansu Peoples R China

ISBN: (纸本)9781538632765

Clinical depression can be characterized by a range of psychological factors, resulting in social, occupational and educational impaired function. Current clinical practice depends almost exclusively on self-report and clinical opinion, risking a range of subjective biases. Such methods are subjective and single in nature, and lack an objective predictor of depression. this project aims at developing a novel method for diagnosis of depression using speech analysis from psychological perspective. It is well known that the Self is not only the cognitive subject, but also the core of personality. In this PhD work, for above reason, classical scientific psychology paradigms are employed on abnormalities of self-related processing in patients from different dimensions of the Self, and speech signal processing methods and machine learning methods are adopted for depressed speech. We believe the method can better capture psychological characteristics of depressed patients, and make a meaningful progress in improving diagnosis accuracy.

关键词： depression mental disorder self-related processing depressed speech joint model

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An optimized low computational algorithm for human fall detection from depth images based on Support Vector machine classification 5

An optimized low computational algorithm for human fall dete...

引用

5th IEEE international conference on signal and Image processing Applications, ICSIPA 2017

作者： Mohd, Mohd Norzali Haji Nizam, Y. Suhaila, S. Mahadi Abdul Jamil, M. Batu Pahat Johor Malaysia Batu Pahat Johor Malaysia

ISBN: (纸本)9781509055593

Systems developed to classify human activities to identify unintentional falls are highly demanding and play an important role in our daily life. Human falls are the main obstacle for elderly people to live independently and it is also a major health concern due to aging population. Different approaches are used to develop human fall detection systems for elderly and people with special needs. the three basic approaches used include some sort of wearable devices, ambient based devices or non-invasive vision based devices using live cameras. Most of such systems are either based on wearable or ambient sensor which is very often rejected by users due to the high false alarm and difficulties in carrying them during their daily life activities. this paper proposes a fall detection system based on an algorithm using combination of machine learning and human activity measurements such as changes of human height and rate of change of the subject during any of the activity. Classification of human fall from other activities of daily life is accomplished using height, changes in velocity and acceleration of the subject extracted from the depth information. Finally position of the subject and SVM classification is used for fall confirmation. From the experimental results, the proposed system was able to achieve an average accuracy of 97.39% with sensitivity of 100% and specificity of 96.61%. © 2017 IEEE.

关键词： Fall detection

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Evaluation of Algorithms for Automatic Classification of Heart Sound signals 5th

Evaluation of Algorithms for Automatic Classification of Hea...

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5th international Work-conference on Bioinformatics and Biomedical Engineering (IWBBIO)

作者： Enrique Perez-Guzman, Ricardo Garcia-Bermudez, Rodolfo Rojas-Ruiz, Fernando Cespedes-Perez, Ariel Ojeda-Riquenes, Yudelkis Univ Las Tunas Dept Comp Sci Las Tunas Cuba Tech Univ Manabi Fac Comp Sci Manta Ecuador Univ Granada Dept Comp Architecture & Technol CITIC Granada Spain Teaching Hosp Ernesto Guevara de la Serna Las Tunas Cuba

ISBN: (纸本)9783319561486;9783319561479

Auscultation is the primary tool for detection and diagnosis of cardiovascular diseases in hospitals and home visits. this fact has led in the recent years to the development of automatic methods for heart sound classification, thus allowing for detecting cardiovascular pathologies in an effective way. the aim of this paper is to review recent methods for automatic classification and to apply several signal processing techniques in order to evaluate them in the PhysioNet/CinC Challenge 2016 results. For this purpose, the records of the open database PysioNet/Computing are modified by segmentation or filtering methods and the results were tested using the challenge best ranked algorithms. Results show that an adequate preprocessing of data and subsequent feature selection may improve the performance of machine learning and classification techniques.

关键词： Heart sounds Phonocardiogram signal processing Classification algorithms Preprocessing

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Automatic detection of ischemic stroke based on scaling exponent electroencephalogram using extreme learning machine 5

Automatic detection of ischemic stroke based on scaling expo...

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5th international conference on Science and Engineering in Mathematics, Chemistry and Physics 2017, ScieTech 2017

作者： Adhi, H.A. Wijaya, S.K. Prawito Badri, C. Rezal, M. Biomedical Engineering Master Program Universitas Indonesia Jakarta Indonesia Faculty of Mathematics and Natural Sciences Universitas Indonesia West Java Indonesia

Stroke is one of cerebrovascular diseases caused by the obstruction of blood flow to the brain. Stroke becomes the leading cause of death in Indonesia and the second in the world. Stroke also causes of the disability. Ischemic stroke accounts for most of all stroke cases. Obstruction of blood flow can cause tissue damage which results the electrical changes in the brain that can be observed through the electroencephalogram (EEG). In this study, we presented the results of automatic detection of ischemic stroke and normal subjects based on the scaling exponent EEG obtained through detrended fluctuation analysis (DFA) using extreme learning machine (ELM) as the classifier. the signal processing was performed with 18 channels of EEG in the range of 0-30 Hz. Scaling exponents of the subjects were used as the input for ELM to classify the ischemic stroke. the performance of detection was observed by the value of accuracy, sensitivity and specificity. the result showed, performance of the proposed method to classify the ischemic stroke was 84 % for accuracy, 82 % for sensitivity and 87 % for specificity with 120 hidden neurons and sine as the activation function of ELM. © Published under licence by IOP Publishing Ltd.

关键词： Electroencephalography

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Impact of target variable distribution type over the regression analysis in wind turbine data

Impact of target variable distribution type over the regress...

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5th IEEE international Work conference on Bio-Inspired Intelligence, IWOBI 2017

作者： Blanco, M.-Alejandro Sole-Casals, Jordi Marti-Puig, Pere Cardenas Isaac Justicia, Juan Jose Cusido, Jordi Data and Signal Processing Group U Science Tech University of Vic - Central University of Catalonia C. de la Laura 13 08500 Vic Catalonia Spain Smartive-ITESTIT SL Catalonia Spain

ISBN: (纸本)9781538608500

the Wind sector has roughly 2200M euros of profit losses due to wind turbine failures and these failures do not contribute to the goal of reducing greenhouse gas emissions of many states. the 25-35% of the generation costs are operation and maintenance services. To lower this ratio, the wind turbine industry is backing on the machine learning techniques over SCADA data. signal trending analysis supported on linear regression models presents the problem of how to carefully choose the right target variable, which reproduces as close as possible the behavior of a failure from a component. this document evaluates the impact of that choice by comparing as target different variables with discrete-non normal distribution, commonly selected by feature selection methods, versus variables that are continuous over time with a near normal distribution. Experimental results on real data show the use of continuous target variables selected by human expert on the field give better results than the use of targets obtained through feature selection algorithm. © 2017 IEEE.

关键词： Wind power

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A Multi-Key Compressed Sensing and machine learning Privacy Preserving Computing Scheme 5

A Multi-Key Compressed Sensing and Machine Learning Privacy ...

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5th international Symposium on Computational and Business Intelligence (ISCBI)

作者： Fakhr, Mohamed Waleed Arab Acad Sci Technol & Maritime Transport Coll Engn Dept Comp Engn Cairo Egypt

ISBN: (纸本)9781538617724

Recently there has been a huge interest in secure and private cloud computing. In particular;to perform signal processing and machine learning tasks in the encrypted domain. Homomorphic encryption offers provably secure, asymmetric encryption solution to this problem, however, it comes with a high storage and computation cost. Compressed sensing (CS) and random projection (RP) approaches are much lighter;however, they lack privacy since the encryption uses a symmetric key which is the random projection matrix. A multi-key, compressed sensing encryption approach is proposed in this paper for performing basic generic computations. the computing architecture consists of a User, a Cloud (which stores encrypted data from an Owner), and a Trusted third Party (TTP) which is responsible for distributing the random CS keys. the TTP also trains two machine learning modules;ML1 and ML2. ML1, used at the cloud, takes as input the multi-key encrypted data and produces an intermediate encrypted result. ML2, available at the user side, decrypts the results. this novel approach is much cheaper than homomorphic encryption in terms of data expansion, storage as well as encryption time. Also, it offers the privacy of the multi-keys. the proposed approach is applied on 2 generic computing tasks;namely, squared Euclidean distance and dot product. the developed approach is tested on the COREL image classification task using the squared Euclidean distance and on an autoregressive (AR) stock prediction task using the dot product.

关键词： Compressed Sensing Encryption Homomorphic Encryption machine learning Multi-Key Encryption Privacy Preserving Computing

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ICA based Feature learning and Feature Selection 5

ICA based Feature Learning and Feature Selection

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5th international conference on Electronic Devices, Systems and Applications (ICEDSA)

作者： Ibrahim, Marwa Farouk Ibrahim Al-Jumaily, Adel Ali Univ Technol Sydney Sch Elect Mech & Mechatron Syst Sydney NSW Australia

ISBN: (纸本)9781509053063

Feature extraction is playing a major role in bio signal processing. Feature identification and selection has two approaches. the common approach is engineering handcraft which is based on user experience and application area. While the other approach is feature learning that based on making the system identify and select the best features suit the application. the idea behind feature learning is to avoid dealing with any feature extraction or reduction algorithms and to train the suggested model on learning with avoiding the exposure to feature extraction which is mainly based on researcher experience. In this paper, Independent component analysis (ICA) will be implemented as a feature learning technique to learn the model extract the features from the input data. Deep learning approach will be proposed by implementing ICA to learn features. In the proposed model, the raw data will be read then represented by using different signal representation as Spectrogram, Wavelet and Wavelet Packet. then, the new represented data will be fed to Independent component analysis layer to generate features and finally, the performance of the suggested scheme will be evaluated by applying different classifiers such as Support Vector machine, Extreme learning machine and Discriminate Analysis. And As an improving step for the results, classifier fusion layer will be implemented to select the most accurate result for both training and testing set. Classifier fusion layer resulted in a promising training and testing accuracies. On the other side, Feature Selection is the process of selecting subset of features from a pool of features.

关键词： unlabeled data unsupervised learning feature learning deep learning bio-signal processing independent componenet analysis indices support vector machine extreme learning machine discriminant analysis classifier fusion

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