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19th International Conference on Methods and Models in Automation and Robotics (MMAR)

作者： Juszkiewicz, Lukasz Wroclaw Univ Technol PL-50370 Wroclaw Poland

ISBN: (纸本)9781479950812

This paper presents modification of a speech emotion recognition system for a social robot. Using speaker dependent classifiers with prior speaker identification step was proposed. Emotion recognition is done using global acoustic features of the speech. Six speech signal parameters are computed with the specialised software. The feature extraction is based on calculation of global statistics of those parameters and their derivatives. Correlation based feature selection algorithms are used for reducing feature vector length. For the evaluation of the system three classifiers were used: Bayes Network, Radial Basis Function Network and support vector machine. Six emotions could be recognised by the system. Several experiments were carried out: emotion recognition of the speaker not known to the system, speaker independent recognition of the known speakers, gender dependent classification as well as speaker dependent recognition with prior identification. The results of identification-driven speech emotion recognition show significant improvement compared to the performance of the speaker independent system based on previous research. The use of the tailored speaker dependent recognition system led to increase in the Bayes Network classification accuracy from 74% to 96%, in the support vector machine classification accuracy from 64% to 96% and in the Radial Basis Function Network classification accuracy from 67% to 88%.

关键词： belief networks emotion recognition feature extraction radial basis function networks robots signal classification speaker recognition speech recognition support vector machines Bayes network classifier correlation based feature selection algorithms global acoustic features radial basis function network classifier social robot speaker dependent classifiers speaker identification speech emotion recognition system speech signal parameters support vector machine classifier Accuracy Emotion recognition Feature extraction Robots Speech Speech recognition support vector machines emotion recognition speaker recognition Feature extraction support vector Network Radial basis function networks Robot Speech recognition Bayesian networks Speech speech recognition system signal classification

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Hybrid EEG-EOG System for Intelligent Prosthesis Control based on Common Spatial Pattern Algorithmd

Hybrid EEG-EOG System for Intelligent Prosthesis Control bas...

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IEEE International Conference on Information and Automation (ICIA)

作者： Yang, Junyou Su, Xiaoying Bai, Dianchun Jiang, Yinlai Yokoi, Hiroshi Shenyang Univ Technol Dept Elect Engn Shenyang Liaoning Provin Peoples R China Univ Electrocommun Dept Intelligent Robot Chofu Tokyo Japan

ISBN: (纸本)9781509041022

A newly brain computer interaction (BCI) system which combined electrooculography (EOG) with electroencephalography (EEG) was designed and realized to make prosthesis control with multicontrol commands come true. Based on two different imaginary tasks, time-frequency spectrum and brain topographic mapping were used to analyze and validate event related synchronization/desychronization (ERS/ERD). Obviously frequency band of EEG is chosen based on common spatial pattern (CSP) algorithm, and then support vector machine (SVM) were implemented to classify data's characteristics. In order to improve the performance of BCI system, the EOG signals caused by eye movements and EEG signals of ERS/ERD were simultaneously provided as the input of hybrid BCI system. In the lab environment, the proposed system was tested on a multi-functional robotic prosthesis. The robot is light in weight and low in power consumption. Taking advantage of both EOG and EEG, recognition accuracy of entire system reaches 91.1%. The experiments results show that feature information of EEG signal which is evoked by motor imagery have obvious spatial specificity. This hybrid system can recognize different motions of the upper limb, whereas the task can be executed effectively and operator controls the prosthesis easily as their own arm.

关键词： EOG signal EEG signal prosthesis common spatial pattern support vector machine classifier

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Classification of micro-CT images using 3D characterization of bone canal patters in human osteogenesis imperfecta

Classification of micro-CT images using 3D characterization ...

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Conference on Medical Imaging - Computer-Aided Diagnosis

作者： Abidin, Anas Z. Jameson, John Molthen, Robert Wismueller, Axel Univ Rochester Dept Biomed Engn Rochester NY 14627 USA Univ Rochester Dept Imaging Sci Rochester NY 14627 USA Ludwig Maximilians Univ Munchen Fac Med Munich Germany Ludwig Maximilians Univ Munchen Inst Clin Radiol Munich Germany Marquette Univ Dept Biomed Engn Milwaukee WI 53233 USA Marquette Univ OREC Milwaukee WI 53233 USA

ISBN: (纸本)9781510607132;9781510607149

Few studies have analyzed the microstructural properties of bone in cases of Osteogenenis Imperfecta (OI), or 'brittle bone disease'. Current approaches mainly focus on bone mineral density measurements as an indirect indicator of bone strength and quality. It has been shown that bone strength would depend not only on composition but also structural organization. This study aims to characterize 3D structure of the cortical bone in high- resolution micro CT images. A total of 40 bone fragments from 28 subjects (13 with OI and 15 healthy controls) were imaged using micro tomography using a synchrotron light source (SR mu CT). Minkowski functionals - volume, surface, curvature, and Euler characteristics - describing the topological organization of the bone were computed from the images. The features were used in a machine learning task to classify between healthy and OI bone. The best classification performance (mean AUC - 0.96) was achieved with a combined 4-dimensional feature of all Minkowski functionals. Individually, the best feature performance was seen using curvature (mean AUC - 0.85), which characterizes the edges within a binary object. These results show that quantitative analysis of cortical bone microstructure, in a computer-aided diagnostics framework, can be used to distinguish between healthy and OI bone with high accuracy.

关键词： Minkowski functionals texture analysis support vector machine classifier bone osteogenesis imperfecta

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Night-time pedestrian classification with histograms of oriented gradients-local binary patterns vectors

Night-time pedestrian classification with histograms of orie...

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20th ITS World Congress

作者： Hurney, Patrick Waldron, Peter Morgan, Fearghal Jones, Edward Glavin, Martin Natl Univ Ireland Galway Sch Elect & Elect Engn Coll Engn & Informat Connaught Automot Res Grp Galway City Ireland Intel Shannon Shannon Clare Ireland Natl Univ Ireland Galway Sch Elect & Elect Engn Coll Engn & Informat Galway City Ireland

The use of night vision systems in vehicles is becoming increasingly common, not just in luxury cars but also in the more cost sensitive sectors. Numerous approaches using infrared sensors have been proposed in the literature to detect and classify pedestrians in low visibility situations. However, the performance of these systems is limited by the capability of the classifier. This paper presents a novel method of classifying pedestrians in far-infrared automotive imagery. Regions of interest are segmented from the infrared frame using seeded region growing. A novel method of filtering the region growing results based on the location and size of the bounding box within the frame is described. This results in a smaller number of regions of interest for classification, leading to a reduced false positive rate. Histograms of oriented gradient features and local binary pattern features are extracted from the regions of interest and concatenated to form a feature for classification. Pedestrians are tracked with a Kalman filter to increase detection rates and system robustness. Detection rates of 98%, and false positive rates of 1% have been achieved on a database of 2000 images and streams of video;this is a 3% improvement on previously reported detection rates.

关键词： pedestrians traffic engineering computing feature extraction image classification infrared detectors automobiles support vector machines image segmentation filtering theory night-time pedestrian classification histogram of oriented gradient-local binary pattern vectors night vision systems high end luxury cars low-cost infrared sensors far infrared automotive image streams support vector machine classifier region of interest RoF captured infrared frame seeded region growing filtering method bounding box ROI reduced false positive rate local binary pattern feature extraction histogram of oriented gradient feature extraction Kalman filter detection rates

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IMAGE RECOGNITION BY LEARNED LINEAR SUBSPACE OF COMBINED BAG-OF-FEATURES AND LOW-LEVEL FEATURES

IMAGE RECOGNITION BY LEARNED LINEAR SUBSPACE OF COMBINED BAG...

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IEEE International Conference on Image Processing

作者： Han, Xian-Hua Chen, Yen-Wei Ruan, Xiang Ritsumeikan Univ Coll Informat Sci & Engn Kusatsu 5258577 Japan Omron Corp Kyoto Japan

ISBN: (纸本)9781424479948

Image category recognition is important to access visual information on the level of objects and scene types. This paper combines different feature representations of images and learn a compact subspace of different features for the automatic recognition of object and scene classes. Compact visual-words and low-level-features object class subspaces are automatically learned from a set of training images by a Regularized Linear Discriminant analysis (RLDA) algorithm, and the extracted RLDA-domain features are used for support vector machine (SVM) classifier. The main contribution of this paper is two folds: i) Different features (bag-of-features and low-level features) is fused for image representation. ii) The compact feature subspaces (low-dimension features) of different features are learned for rendering to SVM classifier, which is computationally efficient for image category. High classification accuracy is demonstrated on object recognition database (Caltech). We confirm that the proposed strategy cam improve accuracy rate compared with state-of-the-art methods for object recognition databases.

关键词： automatic recognition combined bag-of-features feature extraction image recognition image recognition image representation image representation learned linear subspace low-level features object recognition object recognition databases regularized linear discriminant analysis rendering support vector machine classifier support vector machines training images

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Identification of winter land use in temperate agricultural landscapes based on Sentinel-1 and 2 times-series. 38

Identification of winter land use in temperate agricultural ...

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38th IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Denize, Julien Hubert-Moy, Laurence Corgne, Samuel Betbeder, Julie Pottier, Eric Univ Rennes Rennes France IETR UMR 6164 Rennes France LETG UMR 6554 Rennes France Cirad UPR 105 Forests & Soc Montpellier France

ISBN: (纸本)9781538671504

Land cover and land use monitoring, particularly during winter season, is still a major environmental and scientific issue in agricultural areas. From an environmental point of view, the presence and type of vegetation cover in winter have an impact on pollutant transport to water bodies. From a methodological point of view, characterizing spatio-temporal dynamics of winter land cover and land use at a field scale remains a challenge due to the diversity of farming strategies and practices. The objective of this study was to evaluate the potential of optical and SAR time-series to improve the monitoring of winter land use in an area of 130 km(2). For that purpose, Sentinel-1 and 2 time-series were classified using SVM and RF algorithms. Winter land use was identified with an overall accuracy of 81% and a kappa index of 0.77 from a combination of Sentinel-1 and 2 images.

关键词： Agricultural monitoring Multi-temporal classifications support vector machine classifier Random Forest classifier Earth observing sensors optical and SAR time series

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Performance Study of Cyclostationary based Digital Modulation Classification Schemes 9

Performance Study of Cyclostationary based Digital Modulatio...

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9th Int Conf Ind Information Syst (ICIIS2014)

作者： Satija, Udit Manikandan, M. S. Ramkumar, Barathram Indian Inst Technol Sch Elect Sci Bhubaneswar Orissa India

ISBN: (纸本)9781479964994

Automatic Modulation Classification (AMC) is a essential component in Cognitive Radio (CR) for recognizing the modulation scheme. Many modulated signals manifest the property of cyclostationarity as a feature so it can be exploited for classification. In this paper, we study the performance of digital modulation classification technique based on the cyclostationary features and different classifiers such as Neural Network, support vector machine, k-Nearest Neighbor, Naive Bayes, Linear Discriminant Analysis and Neuro-Fuzzy classifier. In this study we considered modulations i.e. BPSK, QPSK, FSK and MSK for classification. All classification methods studied using performance matrix including classification accuracy and computational complexity (time). The robustness of these methods are studied with SNR ranging from 0 to 20dB. Based upon the result we found that combining cyclostationary features with Naive Bayes and Linear Discriminant Analysis classifiers leads to provide better classification accuracy with less computational complexity.

关键词： Bayes methods cognitive radio frequency shift keying fuzzy systems neural nets pattern classification phase shift keying signal classification support vector machines telecommunication computing BPSK FSK MSK QPSK automatic modulation classification classification accuracy computational complexity cyclostationary based digital modulation classification cyclostationary feature digital modulation classification technique k-nearest neighbor classifier linear discriminant analysis classifier modulated signal naive Bayes classifier neural network classifier neuro-fuzzy classifier performance matrix support vector machine classifier Binary phase shift keying Computational complexity Frequency shift keying Neural networks support vector machines frequency shift keying Binary phase shift keying support vector Network complexity classes Cognitive radio Neural network minimum shift keying modulated signal Quadrature phase shift keying telecommunication computing Phase shift keying signal classification Bayes methods Pattern recognition Fuzzy systems

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Video-Based Detection and Analysis of Driver Distraction and Inattention

Video-Based Detection and Analysis of Driver Distraction and...

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1st International Conference on Signal Processing and Integrated Networks (SPIN)

作者： Yun, Yixiao Gu, Irene Y. H. Bolbat, Mikhail Khan, Zulfiqar Hasan Chalmers Univ Technol Dept Signals & Syst S-41296 Gothenburg Sweden

ISBN: (纸本)9781479928668

This paper addresses two issues for mitigating driver distraction/inattention by using novel video analysis techniques: (a) inside an ego vehicle, driver inattention is monitored through first tracking drivers face/eye region using Riemannian manifold-based particle filters, followed by recognition of dynamic eye states using PPCA (probabilistic principal component analysis) and SVM (support vector machine) classifier. Frequencies of eye blinking and eye closure are used as the indication of sleepy and warning sign is then generated for recommendation;(b) outside an ego vehicle, road traffic is also analyzed. Surrounding vehicles (in both directions) are tracked, and their states are analyzed by self-calibrated cameras using view-geometries and road information. Parameters (e.g. distance, velocity, number) of tracked vehicles are estimated on the road ground plane in the 3D world coordinate system. These pieces of information are provided for mitigating drivers inattention. The main novelties of the proposed scheme include facial geometry based eye region detection for eye closure identification, combined tracking and detection of vehicles, new formulae derived in camera self-calibration, and the hybrid system that handles both daytime and nighttime scenarios. Experiments have been conducted on video data in two different types of camera settings, i.e., captured inside and outside a vehicle. Preliminary tests have been conducted, results and performance evaluation have indicated the effectiveness of the proposed methods.

关键词： cameras driver information systems eye gaze tracking geometry particle filtering (numerical methods) principal component analysis support vector machines video signal processing PPCA Riemannian manifold-based particle filters SVM classifier driver distraction driver inattention dynamic eye state recognition ego vehicle eye blinking eye closure probabilistic principal component analysis road information self-calibrated cameras support vector machine classifier video-based analysis video-based detection view-geometries Cameras Face Monitoring Roads support vector machines Three-dimensional displays Vehicles

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THERMAL FACE RECOGNITION BASED ON PHYSIOLOGICAL INFORMATION 26

THERMAL FACE RECOGNITION BASED ON PHYSIOLOGICAL INFORMATION

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26th IEEE International Conference on Image Processing (ICIP)

作者： Lin, Shinfeng D. Chen, Kuanyuan Chen, Wensheng Natl Dong Hwa Univ Dept Comp Sci & Informat Engn Hualien Taiwan

ISBN: (纸本)9781538662496

In this paper, we propose a novel thermal face recognition based on physiological information. The training phase includes preprocessing, feature extraction and classification. In the beginning, the human face can be depicted from the background of thermal image using the Bayesian framework and normalized to uniform size. A grid of 22 thermal points is extracted as a feature vector. These 22 extracted points are used to train Linear support vector machine classifier (linear SVC). The classifier calculates the support vectors and uses them to find the hyperplane for classification. A feature vector of testing image is inputted to the classifier for face recognition. Our contribution is that the proposed method firstly applies temperature information in face recognition. Experimental results prove the effectiveness of the proposed method.

关键词： Face Recognition Thermal Image Bayesian Framework support vector machine classifier

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Distributed Model Architecture for Cuff-Less Blood Pressure Measurement System

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SN Computer Science 2022年第6期3卷 503页

作者： Dave, Tejal Joshi, Maulin Pandya, Utpal Department of Instrumentation and Control Sarvajanik College of Engineering and Technology Sarvajanik University Surat India Department of Electronics and Communication Sarvajanik College of Engineering and Technology Sarvajanik University Surat India

Continuous monitoring of blood pressure (BP) can control hypertension and cardiac diseases prevailing in many people due to luxurious lifestyle. Blood pressure measurement using cuff-based technique provides intermittent measurement and inconvenient for long-term monitoring. This work is focused on estimation of continuous blood pressure from electrocardiogram (ECG) and photoplethysmogram (PPG). The distributed model architecture is proposed to estimate blood pressure from time domain features of ECG and PPG to overcome problems with single model-based approach. The proposed work uses support vector regression and back propagation neural network for training distributed models. During testing phase, extracted features are first passed through trained pre-classifier to divert the signal to one of the trained distributed models that carries out blood pressure estimation. The proposed work is tested on 193 record segments of Multi-parameter Intelligent Monitoring in Intensive Care (MIMIC) database and also on wireless data captured from 87 subjects using prototype device. According to the British Hypertension Society (BHS) standard, the proposed method achieves grade A in the estimation of systolic and diastolic pressure for wireless data. For MIMIC database, proposed model achieves grade B for systolic, whereas grade A for diastolic pressure estimation. The values of mean error and standard deviation by proposed method are within limits of Association for the Advancement of Medical Instrumentation (AAMI) standards for wireless as well as MIMIC data. The results show improvement in the accuracy of blood pressure estimation compared to similar work and indicate high acceptability according to BHS and AAMI standards. The proposed work is helpful in wireless monitoring of patients to track the physiological conditions without interrupting their routine activities. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Cuff-less blood pressure monitoring Electrocardiogram (ECG) Photoplehysmogram (PPG) support vector machine classifier support vector regression (SVR)

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