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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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VirulentPred: a SVM based prediction method for virulent proteins in bacterial pathogens

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BMC BIOINFORMATICS 2008年第1期9卷 1-12页

作者： Garg, Aarti Gupta, Dinesh ICGEB Struct & Computat Biol Grp New Delhi 110067 India

Background: Prediction of bacterial virulent protein sequences has implications for identification and characterization of novel virulence-associated factors, finding novel drug/vaccine targets against proteins indispensable to pathogenicity, and understanding the complex virulence mechanism in pathogens. Results: In the present study we propose a bacterial virulent protein prediction method based on bi-layer cascade support vector machine (SVM). The first layer SVM classifiers were trained and optimized with different individual protein sequence features like amino acid composition, dipeptide composition (occurrences of the possible pairs of i(th) and i + 1(th) amino acid residues), higher order dipeptide composition (pairs of i(th) and i + 2(nd) residues) and Position Specific Iterated BLAST (PSI-BLAST) generated Position Specific Scoring Matrices (PSSM). In addition, a similarity-search based module was also developed using a dataset of virulent and non-virulent proteins as BLAST database. A five-fold cross-validation technique was used for the evaluation of various prediction strategies in this study. The results from the first layer (SVM scores and PSI-BLAST result) were cascaded to the second layer SVM classifier to train and generate the final classifier. The cascade SVM classifier was able to accomplish an accuracy of 81.8%, covering 86% area in the Receiver Operator Characteristic (ROC) plot, better than that of either of the layer one SVM classifiers based on single or multiple sequence features. Conclusion: VirulentPred is a SVM based method to predict bacterial virulent proteins sequences, which can be used to screen virulent proteins in proteomes. Together with experimentally verified virulent proteins, several putative, non annotated and hypothetical protein sequences have been predicted to be high scoring virulent proteins by the prediction method. VirulentPred is available as a freely accessible World Wide Web server - VirulentPred, at http://bioinfo

关键词： support vector machine support vector machine classifier Independent Dataset Radial Basis Function Kernel Matthews Correlation Coefficient

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A mixture model with a reference-based automatic selection of components for disease classification from protein and/or gene expression levels

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BMC BIOINFORMATICS 2011年第1期12卷 1-18页

作者： Kopriva, Ivica Filipovic, Marko Rudjer Boskovic Inst Div Laser & Atom R&D Zagreb 10000 Croatia

Background: Bioinformatics data analysis is often using linear mixture model representing samples as additive mixture of components. Properly constrained blind matrix factorization methods extract those components using mixture samples only. However, automatic selection of extracted components to be retained for classification analysis remains an open issue. Results: The method proposed here is applied to well-studied protein and genomic datasets of ovarian, prostate and colon cancers to extract components for disease prediction. It achieves average sensitivities of: 96.2 (sd = 2.7%), 97.6% (sd = 2.8%) and 90.8% (sd = 5.5%) and average specificities of: 93.6% (sd = 4.1%), 99% (sd = 2.2%) and 79.4% (sd = 9.8%) in 100 independent two-fold cross-validations. Conclusions: We propose an additive mixture model of a sample for feature extraction using, in principle, sparseness constrained factorization on a sample-by-sample basis. As opposed to that, existing methods factorize complete dataset simultaneously. The sample model is composed of a reference sample representing control and/or case (disease) groups and a test sample. Each sample is decomposed into two or more components that are selected automatically (without using label information) as control specific, case specific and not differentially expressed (neutral). The number of components is determined by cross-validation. Automatic assignment of features (m/z ratios or genes) to particular component is based on thresholds estimated from each sample directly. Due to the locality of decomposition, the strength of the expression of each feature across the samples can vary. Yet, they will still be allocated to the related disease and/or control specific component. Since label information is not used in the selection process, case and control specific components can be used for classification. That is not the case with standard factorization methods. Moreover, the component selected by proposed method as disease specif

关键词： Independent Component Analysis support vector machine classifier Nonnegative Matrix Factorization Sparseness Constraint Independent Component Analysis Algorithm

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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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Automatic segmentation and classification of lung tumour using advance sequential minimal optimisation techniques

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IET IMAGE PROCESSING 2020年第14期14卷 3355-3365页

作者： Vijila Rani, K. Joseph Jawhar, S. Arunachala Coll Engn Women ECE Nagercoil 629203 TN India Arunachala Coll Engn Women EEE Nagercoil 629203 TN India

A chronic disorder caused by abnormal growth of the lung cells in the pulmonary tumour. This study suggests a modern automated approach to improve efficiency and decrease the difficulty of lung tumour diagnosis. The proposed algorithm for lung tumour sensing consists of four phases: pre-processing, segmentation, extraction, and characteristics classification. The first stage is the image acquisition here input lung image is read and then resized. The second stage is the image pre-processing here Perona-Malik diffusion with unsharp masking filter is proposed for enhancement purposes. The third stage is the segmentation here the improved histogram-based fast 2D Otsu's thresholding is proposed for lung tumour segmentation purposes. Finally, linear discriminant analysis classifier, support vector machine (SVM) classifier, SVM-sequence minimal optimisation classifier, Naive Bayes classifier, SVM-advance sequence minimal optimisation (SVM-ASMO) classification [proposed] included in the various classifier groups adopted in this report. Overall performance accuracy of 0.962 is obtained using the proposed SVM-ASMO method that helps to diagnose the cancer cells using the feature extraction process, which is done automatically. The specificity, precision, recall, and F1 score of the proposed method is found to be a value of 0.984, 0.974, 0.98, and 0.984,respectively.

关键词： pattern classification medical image processing image segmentation cancer image classification Bayes methods feature extraction computerised tomography optimisation support vector machines image enhancement tumours lung automatic segmentation advance sequential minimal optimisation techniques lung cells pulmonary tumour successful treatment planning early tumour detection lung tumour diagnosis lung tumour sensing characteristics classification image acquisition here input lung image improved histogram-based fast 2D Otsu's thresholding lung tumour segmentation purposes linear discriminant analysis classifier support vector machine classifier SVM-sequence minimal optimisation classifier SVM-advance sequence minimal optimisation classification

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Hot spot method for pedestrian detection using saliency maps, discrete Chebyshev moments and support vector machine

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IET IMAGE PROCESSING 2018年第7期12卷 1284-1291页

作者： Lahouli, Ichraf Karakasis, Evangelos Haelterman, Robby Chtourou, Zied De Cubber, Geert Gasteratos, Antonios Attia, Rabah Mil Acad Tunisia VRIT Res Grp Nabeul 8000 Tunisia Royal Mil Acad Ave Renaissance 30 B-1000 Brussels Belgium Tunisia Polytech Sch SERCOM Lab Rue El Khawarizmi Tunis 2078 La Marsa Tunisia Democritus Univ Thrace Vasilissis Sofias 12 GR-67100 Xanthi Greece

The increasing risks of border intrusions or attacks on sensitive facilities and the growing availability of surveillance cameras lead to extensive research efforts for robust detection of pedestrians using images. However, the surveillance of borders or sensitive facilities poses many challenges including the need to set up many cameras to cover the whole area of interest, the high bandwidth requirements for data streaming and the high-processing requirements. Driven by day and night capabilities of the thermal sensors and the distinguished thermal signature of humans, the authors propose a novel and robust method for the detection of pedestrians using thermal images. The method is composed of three steps: a detection which is based on a saliency map in conjunction with a contrast-enhancement technique, a shape description based on discrete Chebyshev moments and a classification step using a support vector machine classifier. The performance of the method is tested using two different thermal datasets and is compared with the conventional maximally stable extremal regions detector. The obtained results prove the robustness and the superiority of the proposed framework in terms of true and false positives rates and computational costs which make it suitable for low-performance processing platforms and real-time applications.

关键词： pedestrians support vector machines reliability video surveillance cameras image sensors temperature sensors temperature measurement infrared imaging image enhancement image classification hot spot method pedestrian detection saliency map discrete Chebyshev moment border intrusion surveillance camera data streaming thermal sensor thermal signature thermal imaging contrast-enhancement technique shape description support vector machine classifier image classification maximally stable extremal region detector

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A Hybrid Intelligent Model for Deterministic and Quantile Regression Approach for Probabilistic Wind Power Forecasting

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IEEE TRANSACTIONS ON POWER SYSTEMS 2014年第4期29卷 1663-1672页

作者： Ul Haque, Ashraf Nehrir, M. Hashem Mandal, Paras Teshmont Consultants LP Power Syst Study Grp Calgary AB T2R 0C5 Canada Montana State Univ Elect & Comp Engn Dept Bozeman MT 59717 USA Univ Texas El Paso Dept Ind Mfg & Syst Engn El Paso TX 79968 USA

With rapid increase in wind power penetration into the power grid, wind power forecasting is becoming increasingly important to power system operators and electricity market participants. The majority of the wind forecasting tools available in the literature provide deterministic prediction, but given the variability and uncertainty of wind, such predictions limit the use of the existing tools for decision-making under uncertain conditions. As a result, probabilistic forecasting, which provides information on uncertainty associated with wind power forecasting, is gaining increased attention. This paper presents a novel hybrid intelligent algorithm for deterministic wind power forecasting that utilizes a combination of wavelet transform (WT) and fuzzy ARTMAP (FA) network, which is optimized by using firefly (FF) optimization algorithm. In addition, support vector machine (SVM) classifier is used to minimize the wind power forecast error obtained from WT+FA+FF. The paper also presents a probabilistic wind power forecasting algorithm using quantile regression method. It uses the wind power forecast results obtained from the proposed hybrid deterministic WT+FA+FF+SVM model to evaluate the probabilistic forecasting performance. The performance of the proposed forecasting model is assessed utilizing wind power data from the Cedar Creek wind farm in Colorado.

关键词： Deterministic and probabilistic wind power forecasting firefly fuzzy ARTMAP support vector machine classifier wavelet transform

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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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Intersection detection and recognition for autonomous urban driving using a virtual cylindrical scanner

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IET INTELLIGENT TRANSPORT SYSTEMS 2014年第3期8卷 244-254页

作者： Li, Qingquan Chen, Long Zhu, Quanwen Li, Ming Zhang, Qun Ge, Shuzhi Sam Shenzhen Univ Shenzhen Key Lab Spatial Smart Sensing & Serv Shenzhen Guangdong Peoples R China Sun Yat Sen Univ Sch Mobile Informat Engn Zhuhai Guangdong Peoples R China Wuhan Univ Sch Geodesy & Geomat Wuhan 430072 Hubei Peoples R China Wuhan Univ Comp Sch Wuhan 430072 Hubei Peoples R China Natl Univ Singapore Dept Elect & Comp Engn Singapore 117548 Singapore

In this study, the authors propose an effective real-time approach for intersection detection and recognition during autonomous driving in an unknown urban environment. The authors approach use point cloud data acquired by a three-dimensional laser scanner mounted on the vehicle. Intersection detection and recognition are formulated as a classification problem whereby roads are classified as segments or intersections and intersections are subclassified as T-shaped or +-shaped. They first construct a novel model called a virtual cylindrical scanner for efficient feature-level representation of the point cloud data. Then they use support vector machine classifiers to resolve the classification problem according to the features extracted. A series of experiments on real-world data sets and in a simulation environment demonstrate the effectiveness and robustness of the authors approach, even in highly dynamic urban environment. They also performed simulation experiments to investigate effects of several critical factors on their proposed approach, such as other vehicles on the road and the advance detection distance.

关键词： automated highways data acquisition feature extraction image classification optical scanners support vector machines shape recognition autonomous urban driving virtual cylindrical scanner real-time approach intersection detection intersection recognition unknown urban environment point cloud data acquisition three-dimensional laser scanner road classification T-shaped intersections +-shaped intersections feature-level representation support vector machine classifier feature extraction real-world data sets simulation environment dynamic urban environment

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BLProt: prediction of bioluminescent proteins based on support vector machine and relieff feature selection

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BMC BIOINFORMATICS 2011年第1期12卷 1-7页

作者： Kandaswamy, Krishna Kumar Pugalenthi, Ganesan Hazrati, Mehrnaz Khodam Kalies, Kai-Uwe Martinetz, Thomas Med Univ Lubeck Inst Neuro & Bioinformat D-23538 Lubeck Germany Med Univ Lubeck Grad Sch Comp Med & Life Sci D-23538 Lubeck Germany King Abdullah Univ Sci & Technol Biosci Core Lab Bioinformat Grp Thuwal Saudi Arabia Med Univ Lubeck Inst Signal Proc D-23538 Lubeck Germany Med Univ Lubeck Inst Biol Ctr Struct & Cell Biol Med D-23538 Lubeck Germany

Background: Bioluminescence is a process in which light is emitted by a living organism. Most creatures that emit light are sea creatures, but some insects, plants, fungi etc, also emit light. The biotechnological application of bioluminescence has become routine and is considered essential for many medical and general technological advances. Identification of bioluminescent proteins is more challenging due to their poor similarity in sequence. So far, no specific method has been reported to identify bioluminescent proteins from primary sequence. Results: In this paper, we propose a novel predictive method that uses a support vector machine (SVM) and physicochemical properties to predict bioluminescent proteins. BLProt was trained using a dataset consisting of 300 bioluminescent proteins and 300 non-bioluminescent proteins, and evaluated by an independent set of 141 bioluminescent proteins and 18202 non-bioluminescent proteins. To identify the most prominent features, we carried out feature selection with three different filter approaches, ReliefF, infogain, and mRMR. We selected five different feature subsets by decreasing the number of features, and the performance of each feature subset was evaluated. Conclusion: BLProt achieves 80% accuracy from training (5 fold cross-validations) and 80.06% accuracy from testing. The performance of BLProt was compared with BLAST and HMM. High prediction accuracy and successful prediction of hypothetical proteins suggests that BLProt can be a useful approach to identify bioluminescent proteins from sequence information, irrespective of their sequence similarity. The BLProt software is available at http://***/tools-demos/bioluminescent%20protein/BLProt

关键词： support vector machine support vector machine classifier Feature Subset Training Error Luciferin

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