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Efficient and high-performance pedestrian detector implementation for intelligent vehicles

IET INTELLIGENT TRANSPORT SYSTEMS 2016年第6期10卷 438-444页

作者： Said, Yahia Atri, Mohamed Univ Monastir Fac Sci Monastir Lab Elect & Microelect E E Monastir 5000 Tunisia

Human detection is exploited as a key operation in many applications such as automotive safety, intelligent vehicles, assisted living, and video surveillance. Consequently, there is a significant advancement in this area of research in the past years and a vast literature. In this study, the authors propose a pedestrian detection system which relies on sliding covariance matrix feature descriptor combined with a support vector machine classifier. The proposed framework is implemented onto field programmable gate array prototyping boards. Experimental results using the standard Institut National de Recherche en Informatique et en Automatique (INRIA) pedestrian benchmark dataset show that the proposed architecture achieved outstanding processing performances with high detection accuracy when compared with state-of-the-art methods.

关键词： intelligent transportation systems covariance matrices field programmable gate arrays computer vision high-performance pedestrian detector implementation intelligent vehicles human detection pedestrian detection system sliding covariance matrix feature descriptor support vector machine classifier field programmable gate array prototyping boards standard INRIA pedestrian benchmark dataset

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Evaluation of handwriting kinematics and pressure for differential diagnosis of Parkinson's disease

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ARTIFICIAL INTELLIGENCE IN MEDICINE 2016年第Feb.期67卷 39-46页

作者： Drotar, Peter Mekyska, Jiri Rektorova, Irena Masarova, Lucia Smekal, Zdenek Faundez-Zanuy, Marcos Brno Univ Technol Dept Telecommun Tech 12 Brno 61200 Czech Republic St Anns Univ Hosp Fac Med Dept Neurol 1 Pekarska 664 Brno 66591 Czech Republic Escola Univ Politecn Mataro Signal Proc Grp TecuocampusAvda Ernest Llunch 32 Mataro 08302 Spain

Objective: We present the PaHaW Parkinson's disease handwriting database, consisting of handwriting samples from Parkinson's disease (PD) patients and healthy controls. Our goal is to show that kinematic features and pressure features in handwriting can be used for the differential diagnosis of PD. Methods and material: The database contains records from 37 PD patients and 38 healthy controls performing eight different handwriting tasks. The tasks include drawing an Archimedean spiral, repetitively writing orthographically simple syllables and words, and writing of a sentence. In addition to the conventional kinematic features related to the dynamics of handwriting, we investigated new pressure features based on the pressure exerted on the writing surface. To discriminate between PD patients and healthy subjects, three different classifiers were compared: K-nearest neighbors (K-NN), ensemble AdaBoost classifier, and support vector machines (SVM). Results: For predicting PD based on kinematic and pressure features of handwriting, the best performing model was SVM with classification accuracy of P-acc = 81.3% (sensitivity P-sen = 87.4% and specificity of P-spe = 80.9%). When evaluated separately, pressure features proved to be relevant for PD diagnosis, yielding P-acc = 82.5% compared to P-acc = 75.4% using kinematic features. Conclusion: Experimental results showed that an analysis of kinematic and pressure features during handwriting can help assess subtle characteristics of handwriting and discriminate between PD patients and healthy controls. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Decision support system support vector machine classifier Handwriting database Handwriting pressure Parkinson's disease PD dysgraphia

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Abrupt skin lesion border cutoff measurement for malignancy detection in dermoscopy images

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BMC BIOINFORMATICS 2016年第13-Sup期17卷 95-115页

作者： Kaya, Sertan Bayraktar, Mustafa Kockara, Sinan Mete, Mutlu Halic, Tansel Field, Halle E. Wong, Henry K. Univ Cent Arkansas Dept Comp Sci Conway AR 72035 USA Univ Arkansas Dept Bioinformat Little Rock AR 72204 USA Texas A&M Commerce Dept Comp Sci & Informat Syst Commerce TX USA Univ Arkansas Med Sci Dept Dermatol Little Rock AR 72205 USA

Background: Automated skin lesion border examination and analysis techniques have become an important field of research for distinguishing malignant pigmented lesions from benign lesions. An abrupt pigment pattern cutoff at the periphery of a skin lesion is one of the most important dermoscopic features for detection of neoplastic behavior. In current clinical setting, the lesion is divided into a virtual pie with eight sections. Each section is examined by a dermatologist for abrupt cutoff and scored accordingly, which can be tedious and subjective. Methods: This study introduces a novel approach to objectively quantify abruptness of pigment patterns along the lesion periphery. In the proposed approach, first, the skin lesion border is detected by the density based lesion border detection method. Second, the detected border is gradually scaled through vector operations. Then, along gradually scaled borders, pigment pattern homogeneities are calculated at different scales. Through this process, statistical texture features are extracted. Moreover, different color spaces are examined for the efficacy of texture analysis. Results: The proposed method has been tested and validated on 100 (31 melanoma, 69 benign) dermoscopy images. Analyzed results indicate that proposed method is efficient on malignancy detection. More specifically, we obtained specificity of 0.96 and sensitivity of 0.86 for malignancy detection in a certain color space. The F-measure, harmonic mean of recall and precision, of the framework is reported as 0.87. Conclusions: The use of texture homogeneity along the periphery of the lesion border is an effective method to detect malignancy of the skin lesion in dermoscopy images. Among different color spaces tested, RGB color space's blue color channel is the most informative color channel to detect malignancy for skin lesions. That is followed by YCbCr color spaces Cr channel, and Cr is closely followed by the green color channel of RGB color space.

关键词： support vector machine support vector machine classifier Chain Code Texture Feature Extraction Radius Size

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A fast and accurate algorithm for eye opening or closing detection based on local maximum vertical derivative pattern

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2016年第6期24卷 5124-5134页

作者： Tafreshi, Marzieh Fotouhi, Ali Mohammad Tafresh Univ Dept Elect Engn Tafresh *** Iran

In this paper, a fast and accurate algorithm is proposed to recognize open and closed eye states. In the proposed algorithm, firstly, a hierarchical preprocessing stage is used to detect eye areas. This stage employs Haar features to detect face area, color and intensity mappings to extract eye candidate areas, and some simple geometrical relations for final decision of the eye regions. In the second stage of algorithm for detecting eye state, a new proposed descriptor based on histogram of local maximum vertical derivative pattern (LMVDP) in eye areas is extracted and applied to support vector machine (SVM) classifier. Proposed descriptor, while having low computational complexity, is well defined to describe eye features and hence can distinguish well between open and closed eyes. Experimental results on test images show that the proposed algorithm can correctly detect the eye state by the ratio of 98.2%, which is higher than other similar algorithms.

关键词： Eye detection eye state recognition local maximum vertical derivative pattern support vector machine classifier

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Detecting and positioning of traffic incidents via video-based analysis of traffic states in a road segment

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IET INTELLIGENT TRANSPORT SYSTEMS 2016年第6期10卷 428-437页

作者： Ren, Jianqiang Chen, Yangzhou Xin, Le Shi, Jianjun Li, Baotong Liu, Yinan Beijing Univ Technol Coll Metropolitan Transportat Beijing Peoples R China Langfang Teachers Univ Coll Math & Informat Sci Langfang Peoples R China Beijing Key Lab Transportat Engn Beijing Peoples R China

Non-recurrent traffic incidents (accidents, stalled vehicles and spilled loads) often bring about traffic congestion and even secondary accidents. Detecting and positioning them quickly and accurately has important significance for early warning, timely incident-disposal and speedy congestion-evacuation. This study proposes a video-based detecting and positioning method by analysing distribution characteristics of traffic states in a road segment. Each lane in the monitored segment is divided into a cluster of cells. Traffic parameters in each cell, including flow rate, average travel speed and average space occupancy, are obtained by detecting and tracking traffic objects (vehicles and spilled loads). On the basis of the parameters, traffic states in the cells are judged via a fuzzy-identification method. For each congested cell, a feature vector is constructed by taking its state together with states of its upstream and downstream neighbouring cells in the same lane. Then, a support vector machine classifier is trained to detect incident point. If a cell is judged to be corresponding to an incident point at least for two successive time periods, an incident is detected and its position is calculated based on the identity number of the cell. Experiments prove the efficiency and practicability of the proposed method.

关键词： traffic engineering computing video signal processing road accidents object detection road vehicles fuzzy set theory support vector machines image classification support vector machine classifier downstream neighbouring cells upstream neighbouring cells feature vector fuzzy-identification method traffic state distribution characteristics analysis video-based detecting method speedy congestion-evacuation timely incident-disposal early warning traffic congestion spilled loads stalled vehicles accidents nonrecurrent traffic incidents road segment traffic state video-based analysis traffic incident positioning traffic incident detection

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Computer assisted optical screening of human ovarian cancer using Raman spectroscopy

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PHOTODIAGNOSIS AND PHOTODYNAMIC THERAPY 2016年第0期15卷 94-99页

作者： Ullah, Irfan Ahmad, Iftikhar Nisar, Hasan Khan, Saranjam Ullah, Rahat Rashid, Rashad Mahmood, Hassan Pakistan Inst Engn & Appl Sci Islamabad 45650 Pakistan Natl Inst Lasers & Optron NILOP Islamabad 45650 Pakistan Citi Lab Islamabad Pakistan

Conventional screening tools for ovarian cancer such as cancer antigen (CA-125) and trans-pelvic ultrasound have poor sensitivity and specificity, indicating the need for better and more reliable screening methodologies. Here, we investigate the capability of Raman spectroscopy as a screening technique for ovarian cancer. Raman spectra from the blood serum of healthy control and ovarian cancer subjects were measured. Highly significant Raman shifts (p < 0.0001) and intensity variations were observed in the cancer group as compared to the healthy group. These spectral differences were exploited by support vector machine classifier towards computer assisted classification. Calculated evaluation metrics such as sensitivity (=90), specificity (-100), positive predictive value (=100) and negative predictive value (=87.5) for such classification indicated that these results are promising, with potential future application of Raman spectroscopy for ovarian cancer screening. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Raman spectroscopy Ovarian cancer support vector machine classifier Computer assisted classification

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Spatiotemporal feature extraction for facial expression recognition

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IET IMAGE PROCESSING 2016年第7期10卷 534-541页

作者： Kamarol, Siti Khairuni Amalina Jaward, Mohamed Hisham Parkkinen, Jussi Parthiban, Rajendran Monash Univ Malaysia Sch Engn Elect & Comp Syst Engn Petaling Jaya 46150 Selangor Malaysia Univ Eastern Finland Sch Comp Joensuu 80100 Finland

A key issue regarding feature extraction is the capability of a technique to extract distinctive features to represent facial expressions while requiring a low computational complexity. In this study, the authors propose a novel approach for appearance-based facial feature extraction to perform the task of facial expression recognition on video sequences. The proposed spatiotemporal texture map (STTM) is capable of capturing subtle spatial and temporal variations of facial expressions with low computational complexity. First, face is detected using Viola-Jones face detector and frames are cropped to remove unnecessary background. Facial features are then modelled with the proposed STTM, which uses the spatiotemporal information extracted from three-dimensional Harris corner function. A block-based method is adopted to extract the dynamic features and represent the features in the form of histograms. The features are then classified into classes of emotion by the support vector machine classifier. The experimental results demonstrate that the proposed approach shows superior performance compared with the state-of-the-art approaches with an average recognition rate of 95.37, 98.56, and 84.52% on datasets containing posed expressions, spontaneous micro-expressions, and close-to-real-world expressions, respectively. They also show that the proposed algorithm requires low computational cost.

关键词： face recognition emotion recognition image sequences image classification image representation video signal processing support vector machines image texture spatiotemporal feature extraction facial expression recognition appearance-based facial feature extraction video sequences spatiotemporal texture map STTM spatial facial expression variation temporal facial expression variation low computational complexity Viola-Jones face detector facial feature modelling 3D Harris corner function block-based method feature representation support vector machine classifier posed expressions spontaneous microexpressions close-to-real-world expressions

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CURVELET FUSION ENHACEMENT BASED EVALUATION OF DIABETIC RETINOPATHY BY THE IDENTIFICATION OF EXUDATES IN OPTIC COLOR FUNDUS IMAGES

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BIOMEDICAL ENGINEERING-APPLICATIONS BASIS COMMUNICATIONS 2016年第6期28卷 1650046-1-1650046-10页

作者： Bhargavi, V. Ratna Senapati, Ranjan K. KL Univ Dept Elect & Commun Engn Guntur 522502 Andhra Pradesh India

Rapid growth of Diabetes mellitus in people causes damage to posterior part of eye vessel structures. Diabetic retinopathy (DR) is an important hurdle in diabetic people and it causes lesion formation in retina due to retinal vessel structures damage. Bright lesions (BLs) or exudates are initial clinical signs of DR. Early BLs detection can help avoiding vision loss. The severity can be recognized based on number of BLs formed in the color fundus image. Manually diagnosing a large amount of images is time consuming. So a computerized DR grading and BLs detection system is proposed. In this paper for BLs detection, curvelet fusion enhancement is done initially because bright objects maps to largest coeffcients in an image by utilizing the curvelet transform, so that BLs can be recognized in the retina easily. Then optic disk (OD) appearance is similar to BLs and vessel structures are barriers for lesion exact detection and moreover OD falsely classified as BLs and that increases false positives in classification. So these structures are segmented and eliminated by thresholding techniques. Various features were obtained from detected BLs. Publicly available databases are used for DR severity testing. 260 fundus images were used for the performance evaluation of proposed work. The support vector machine classifier (SVM) used to separate fundus images in various levels of DR based on feature set extracted. The proposed system that obtained the statistical measures were sensitivity 100%, specificity 95.4% and accuracy 97.74%. Compared to existing state-of-art techniques, the proposed work obtained better results in terms of sensitivity, specificity and accuracy.

关键词： Diabetic retinopathy Bright lesions Curvelet fusion Feature extraction Classification Segmentation support vector machine classifier

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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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Parameterization of Limbus Distance in Van Herick Method's Angle Closure Glaucoma Diagnosis 7

Parameterization of Limbus Distance in Van Herick Method's A...

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7th International Conference on Information Communication Technology for Embedded Systems (IC-ICTES)

作者： Ardakani, I. S. Kongprawechnon, W. Theeramunkong, T. Bunnun, P. Isshiki, T. Manassakorn, A. Thammasat Univ Sirindhorn Int Inst Technol Sch ICT Pathum Thani Thailand Natl Elect & Comp Technol Ctr Adv Automat & Elect Res Unit Pathum Thani Thailand Tokyo Inst Technol Global Sci Informat & Comp Ctr Dept Commun & Comp Engn Authenticat & Networking GrpICT Support Div Tokyo Japan Chulalongkorn Univ Fac Med Dept Ophthalmol Bangkok Thailand King Chulalongkorn Mem Hosp Thai Red Cross Soc Bangkok Thailand

ISBN: (纸本)9781509022489

As a non-invasive angle closure glaucoma diagnosis procedure, Van Herick's slit-lamp Limbal Anterior Chamber Depth Estimation (LACDE) technique is preferably the current standard screening method preliminarily performed at hospitals and eye clinics. However, accuracy of depth estimation is very much dependent on the slit projection distance from the limbus (LD). This study investigated the relation and designed a parametric model to reduce the inconsistencies between results and improving the Van Herick's method accuracy. Additionally, two different types of classifiers such as support vector machine (SVM) and Decision Tree with addition of LD as a predictor were trained and tested for comparison with parametric model. In the end acquired results clearly showed the effectiveness of LD consideration in LACDE, which lead to improvement in the experimental results.

关键词： Limbal Anterior Chamber Depth Measurement Van Herick Method support vector machine classifier Decision Tree classifier

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