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检索条件"主题词=Support Vector Machine Classifier "

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Multi-temporal classification of TerraSAR-X data for wetland vegetation mapping

Multi-temporal classification of TerraSAR-X data for wetland...

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Conference on Remote Sensing for Agriculture, Ecosystems, and Hydrology XV part of the 20th International Symposium on Remote Sensing

作者： Betbeder, Julie Rapinel, Sebastien Corpetti, Thomas Pottier, Eric Corgne, Samuel Hubert-Moy, Laurence Univ Rennes 2 LETG Rennes COSTEL UMR LETG OSUR 6554 Pl Recteur Henri Le Moal F-35043 Rennes France Univ Rennes 1 IETR UMR CNRS 6164 F-35042 Rennes France

ISBN: (纸本)9780819497567

This paper is concerned with vegetation wetland mapping using multi-temporal SAR imagery. Whilst wetlands play a key role in controlling flooding and nonpoint source pollution, sequestering carbon and providing an abundance of ecological services, knowledge of the flora and fauna of these environments is patchy, and understanding of their ecological functioning is still insufficient for a reliable functional assessment on areas larger than a few ha. The aim of this paper is to evaluate multitemporal TerraSAR-X imagery to map precisely the distribution of vegetation formations within wetlands, in determining seasonally flooded areas of wetlands. A series of six dual-polarization TerraSAR-X images (HH/VV) were acquired in 2012 during dry and wet seasons. Polarimetric and intensity parameters, which present a temporal variation that depends on wetland flooding status and vegetation roughness, were firstly extracted. The parameters were then classified based on support vector machines (SVM) techniques using a specific kernel adapted to the comparison of time-series data. The results show that the Shannon entropy parameter allows discriminating vegetation formations within wetland with more accuracy than intensity parameters.

关键词： Wetlands synthetic aperture radar (SAR) TerraSAR-X imagery multi-temporal classifications support vector machine classifier

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Classification of Kidney Lesions using Gabor Wavelet Texture Features 3

Classification of Kidney Lesions using Gabor Wavelet Texture...

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3rd International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Rana, Shailja Jain, Shruti Virmani, Jitendra Jaypee Univ Informat Technol Waknaghat Himachal Prades India Thapar Univ Patiala Punjab India

ISBN: (纸本)9789380544199

In the present work, characterization of primary benign and primary malignant focal kidney lesions has been carried out using Gabor wavelet texture features. The angiomyolipomas (AMLs) i.e., primary benign kidney lesions and renal cell carcinomas (RCCs) i.e., primary malignant kidney lesions have been considered in the present study. The Gabor features have been computed from region of interest (ROIs) extracted from each image. The total dataset consists of 101ROIs (23 AML ROIs and 78 RCC ROIs) that have been extracted from within the lesions (ignoring the renal sinus part). For the classification task, support vector machine (SVM) has been used. It is observed that the proposed system yields an overall classification accuracy of 90.0 % for differential analysis between primary benign and primary malignant kidney lesions.

关键词： Computer aided diagnosis system Focal kidney lesions Gabor wavelet features Kidney ultrasound images Primary benign lesion Primary malignant lesion support vector machine classifier

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Diabetic Retinopathy Related Lesions Detection and Classification Using machine Learning Technology 15th

Diabetic Retinopathy Related Lesions Detection and Classific...

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15th International Conference on Artificial Intelligence and Soft Computing (ICAISC)

作者： Saha, Rituparna Chowdhury, Amrita Roy Banerjee, Sreeparna West Bengal Univ Technol Dept Comp Sci & Engn Kolkata India

ISBN: (纸本)9783319393841

A novel Computer Aided Diagnosis System for early diagnosis of Diabetic Retinopathy is proposed for the detection and classification of Bright lesion classes and Dark lesion classes of Fundus Retina images using machine learning mechanisms. In the proposed methodology, the detection procedure is based on Fuzzy C Means (FCM) clustering technique to segment the candidate region areas. In the Dark lesion category, attempts are being made to modify the Micro aneurysms detection and Blood vessel elimination with the help of improvised algorithms. For the classification of each Bright and Dark lesion classes a classification system is built using machine learning algorithms namely Naive Bayes and support vector machine. A comparative study between the two machine learning algorithms yield accuracy of 97.0588% for Bright lesion classification using Naive Bayes classifier and accuracy of 88.8889% for Dark lesion classification using support vector machine classifier.

关键词： Diabetic retinopathy Fuzzy C means Nave Bayes classifier support vector machine classifier

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Towards Generic Detection of Unusual Events in Video Surveillance

Towards Generic Detection of Unusual Events in Video Surveil...

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6th IEEE International Conference on Advanced Video and Signal Based Surveillance

作者： Ivanov, Ivan Dufaux, Frederic Ha, Thien M. Ebrahimi, Touradj Ecole Polytech Fed Lausanne Inst Elect Engn Multimedia Signal Proc Grp CH-1015 Lausanne Switzerland

ISBN: (纸本)9781424447558

In this paper, we consider the challenging problem of unusual event detection in video surveillance systems. The proposed approach makes a step toward generic and automatic detection of unusual events in terms of velocity and acceleration. At first, the moving objects in the scene are detected and tracked. A better representation of moving objects trajectories is then achieved by means of appropriate pre-processing techniques. A supervised support vector machine method is then used to train the system with one or more typical sequences, and the resulting model is then used for testing the proposed method with other typical sequences (different scenes and scenarios). Experimental results are shown to be promising. The presented approach is capable of determining similar unusual events as in the training sequences.

关键词： Video surveillance application unusual event trajectory representation feature extraction support vector machine classifier

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Integrating recursive Bayesian estimation with support vector machine to map probability of flooding from multispectral Landsat data

Integrating recursive Bayesian estimation with support vecto...

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

作者： Sarker, Chandrama Alvarez, Luis Mejias Woodley, Alan Queensland Univ Technol Sch Elect Engn & Comp Sci Brisbane Qld Australia

ISBN: (纸本)9781509028962

This paper addresses the challenge of introducing a Bayes rules to measure flood probability from multispectral data. machine learning classifiers were applied to map the extent of flood inundation from multispectral remote sensing imagery. The paper applies Extended support vector machine classifier along with linear spectral un-mixing to obtain the classification output. K-means clustering is applied on pre and post flood images to select SVM training samples from clustering outcome of the most informative spectral band. Experiments were conducted by dividing training and testing samples into two groups. The efficiency of classifier was enhanced by introducing the Bayesian probability measure and performance was assessed by using precision and recall metrics on the pre and post Bayesian flood probability estimation. It has been observed that for some test cases in this study there was a substantial improvement in precision-recall curve with high precision values and low recall rate. The optimal flood probability threshold value has also been easily calculated by calculating and analising iteratively precision and recall.

关键词： remote sensing K-means clustering support vector machine classifier Bayesian estimation

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3rd IEEE Global Conference on Consumer Electronics (GCCE)

作者： Kinoshita, Shohei Ogawa, Takahiro Haseyama, Miki Hokkaido Univ Sch Engn Kita Ku N-13W-8 Sapporo Hokkaido 0608628 Japan Hokkaido Univ Grad Sch Informat Sci & Technol Kita Ku Sapporo Hokkaido 0600814 Japan

ISBN: (纸本)9781479951468

This paper presents popular music estimation based on a topic model using time information and audio features. The proposed method calculates latent topic distribution using Latent Dirichlet Allocation to obtain more accurate music features. In this approach, we also use release date information of each music as time information for concerning the relationship between music trends and each age. Then, by using the obtained latent topic distribution features, the estimation of the popular music becomes feasible based on a support vector machine classifier. Experimental results show the effectiveness of our method.

关键词： music pattern classification support vector machines audio features latent Dirichlet allocation music estimation popular music estimation support vector machine classifier time information topic model Educational institutions Estimation Market research Multiple signal classification Music support vector machines vectors

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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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Predict Driver Fatigue Using Facial Features

Predict Driver Fatigue Using Facial Features

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International Symposium on Advanced Electrical and Communication Technologies (ISAECT)

作者： Berkati, Oussama Srifi, Mohamed Nabil Ibn Tofail Univ Natl Sch Appl Sci Elect & Telecommun Res Grp Kenitra Morocco

ISBN: (纸本)9781538673287

Fatigue on board is amoung the causes of fatal accidents today, however drained driver means that there a metal box without control that threatens lives in our roads. Nowadays, there is no effective non-intrusive method to detect driver tiredness. This paper presents a method for early prediction signs of fatigue during driving. Luckily our face characteristics reflects our current state, therefore we will base on facial features to monitor the driver state. To localize driver's face we had chosen Viola-Jones for faces detection and for face tracking Kanade-Lucas-Tomasi is the best alternative due to their implementation simplicity using just standard phone camera equipped by IR LED. Then all extracted frame characteristics will be presented to SVM for classification that separate the normal state from the critical state. Our objective is to avoid false alerts and early fatigue detection in real-time, for this reason we will combine HOG+SVM, eyes blink rate/duration and PERCLOS. The driver state detection and fatigue alert are not the final steps in our method because a bad reaction can cause disasters that is why we include different road users notification message.

关键词： Fatigue detection support vector machine classifier Viola-Jones Kanade-Lucas-Tomasi Histograms of Oriented Gradients

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Diagnosis of Diabetic Retinopathy Using Morphological Process and SVM classifier

Diagnosis of Diabetic Retinopathy Using Morphological Proces...

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2nd IEEE International Conference on Communications and Signal Processing (ICCSP)

作者： Gandhi, Mahendran Dhanasekaran, R. Syed Ammal Engn Coll Dept Elect & Commun Engn Ramanathapuram Tamil Nadu India

ISBN: (纸本)9781467348669;9781467348652

Diabetic Retinopathy (DR), the most common eye disease of the diabetic patients, occurs when small blood vessels gets damaged in the retina, due to high glucose level. It affects 80% of all patients who have had diabetes for 10 years or more, which can also lead to vision loss. Detection of diabetic retinopathy in advance, protects patients from vision loss. The major symptom of diabetic retinopathy is the exudates. Exudate is a fluid that filters from the circulatory system into lesions or area of inflammation. Detecting retinal fundus diseases in an early stage, helps the ophthalmologists apply proper treatments that might eliminate the disease or decrease the severity of it. This paper focuses on automatic detection of diabetic retinopathy through detecting exudates in colour fundus retinal images and also classifies the rigorousness of the lesions. Decision making of the severity level of the disease was performed by SVM classifier.

关键词： Diabetic Retinopathy Erosion Exudates support vector machine classifier

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THERMAL FACE RECOGNITION UNDER DISGUISED CONDITIONS 18

THERMAL FACE RECOGNITION UNDER DISGUISED CONDITIONS

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International Conference on machine Learning and Cybernetics (IMLC)

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

ISBN: (纸本)9781728128160

A thermal face recognition under disguised conditions using model fusion is proposed. The proposed model fusion has three main approaches: Linear support vector machine classifier (Linear SVC), Convolutional Neural Network (CNN) and Ordinary Least Square (OLS). A grid of 22 thermal face points based on physiological information is extracted for training Linear SVC. The support vectors of testing images using Linear SVC are calculated to find the hyperplane for classification. The novelty is that we firstly apply temperature information in face recognition. In CNN model, the multiple layers of convolutional layer are utilized to extract more effective features. The fully connected layer (FC layer) is trained using the feature matrix of the last convolutional layer. This FC layer is then a classifier to identify the category of the test image. In the training phase, the predicted values from above two approaches are provided to the OLS fir linear regression. The OLS assigns weighting values to these two approaches. This can effectively compensate for the advantages and disadvantages of two approaches. In addition to the comparison with the traditional thermal face recognition, an experiment under disguised conditions was conducted. Experimental results of the proposed method outperform the existing methods.

关键词： Thermal face recognition Thermal image support vector machine classifier Convolutional neural network Ordinary least square

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