The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Conditional Random Field(CRF) has been successfully applied to the hyperspectral image classification. However, it suffers from the availability of large amount of labeled pixels, which is labor- and time-consuming to obtain in practice. In this paper, a semi-supervised CRF(ssCRF) is proposed for hyperspectral image classification with limited labeled pixels. Laplacian Support Vector Machine(LapSVM),...
In this paper, we propose a novel classifier in two-dimensional feature spaces based on the theory of Learning Automata (LA). The essence of our scheme is to search for a separator in the feature space by imposing a LA based random walk in a grid system. To each node in the gird we attach an LA, whose actions are the choice of the edges forming the separator. The walk is self-enclosing, i.e, a new...
A classifier based on the Least Square Support Vector Machine (LS-SVM) with Fruit Fly Optimization Algorithm (FOA) for polarimetirc Synthetic Aperture Radar (SAR) image classification is proposed in this paper. This method uses pixel-based information and region-based information as the features of land cover. The former one comes from the integration of multiple polarimetric parameters obtained by...
This study explores the applicability of the state of the art of deep learning convolutional neural network (CNN) to the classification of CT brain images, aiming at bring images into clinical applications. Towards this end, three categories are clustered, which contains subjects' data with either Alzheimer's disease (AD) or lesion (e.g. tumour) or normal ageing. Specifically, due to the characteristics...
Remote sensing is the method used to detect and measure target characteristics using electromagnetic energy in the form of heat, light and radio waves. Different applications where remote sensing is used are agriculture, disaster management, urban planning, water resource management, etc. The process of producing thematic map from remotely sensed imagery is called image classification. In one or more...
The most widely used classification techniques for whole brain image classification rely on kernel machines such as support vector machines and Gaussian processes, due to their computational efficiency, accurate prediction and suitability to tackle the combination of small sample sizes and high dimensionality that make neuroimaging data a challenging problem. Such methods generally make use of linear...
Panchromatic remote sensing images have useful information about textural classification in land-use and landcover applications. Various methods model texture and extract features for classification tasks. In supervised classification, all of the feature extraction methods try to increase the accuracy of classification and simultaneously decrease the computationally load. At the present work, we use...
In this study, we deemed further to evaluate the performance of Neural Network (NN) and Support Vector Machine (SVM) in classifying the gait patterns between autism and normal children. Firstly, temporal spatial, kinetic and kinematic gait parameters of forty four subjects namely thirty two normal subjects and twelve autism children are acquired. Next, these three category gait parameters acted as...
A robust model is sought for the identification of electroencephalographic (EEG) signals including movements of three distinct parts of the user's arm, namely hand, elbow and shoulder. This study investigates the classification performances of the same upper limb motor movements using various kernel functions of the support vector machine (SVM). Polynomial, linear and radial basis (RBF) functions...
Sparse regression methods have been proven effective in a wide range of signal processing problems such as image compression, speech coding, channel equalization, linear regression and classification. In this paper we develop a new method of hyperspectral image classification based on the sparse unmixing algorithm SUnSAL for which a pixel adaptive L1-norm regularization term is introduced. Our algorithm...
Quality monitoring and prediction plays a key role in improving product quality and achieving automated quality control in manufacturing processes such as the abrasion-resistant material manufacturing process. Traditional methods that rely on the use of first-principle models are difficult to formulate due to the increasing complexity and high dimensionality of manufacturing processes. Data-driven...
This paper reviews the comparative performance of Support Vector Machine (SVM) using four different kernels, i.e., Linear, Polynomial, Radial Basis Function (RBF) and Sigmoid. Overall accuracy (OA), Kappa Index Analysis (KIA), Receiver Operating Characteristic (ROC) and Precision (P) have been considered as evaluation parameters in order to assess the predictive accuracy of SVM. Both high resolution...
Sparsity based signal processing is a relatively new research area which has attracted tremendous interest from researchers. Application areas for sparse signal processing include but are not limited to image processing, pattern recognition and computer vision. This work considers the joint application of sparsity and kernel methods to classification problems. Novel sparsity based classifiers have...
Image classification using kernels have very great importance in remote sensing data. The goal of this work is to efficiently classify the large set of aerial images into different classes. This paper introduces a kernel based classification for aerial images. It uses Grand Unified Regularized Least Square (GURLS) and library for support vector machines (LIBSVM). This paper compares the performance...
Making a correct decision is a difficult task in a Soccer Simulation 2D environment due to the fact that there is a lack of information for each agent. Therefore, coach agent can take role as a mediator for agents to analyze data and inform players about crucial events by sending command messages. This paper proposes a new method to detect the formation of opponents which is not still possible for...
This paper presents an application of machine learning approach for automatic terrain classification suitable for optimal wireless sensor network performance in on-demand deployment. The work entails practical terrain image processing using supervised SVM kernel algorithm moving from gray scale level to color and covering every aspect of a typical terrain image. This paper showcases the integral part...
Predicting mortality of Middle East respiratory syndrome (MERS) patients with identified outcomes is a core goal for hospitals in deciding whether a new patient should be hospitalized or not in the presence of limited resources of the hospitals. We present an oversampling approach that we call Greedy-Based Oversampling Approach (GBOA). We evaluate our approach and compare it against the standard oversampling...
Laser Induced Breakdown Spectroscopy (LIBS) is an analytical technique for rapid chemical sensing that is becoming increasingly in field applications. LIBS is a form of atomic emission spectroscopy. When a high-powered pulsed laser beam is focused on the sample, it produces a plasma that emits wavelengths of light that can be observed using a spectrograph to identify the composition of the target...
Pedestrian detection has been always a challenging problem in computer vision. Numerous approaches based on features extraction and classification have been proposed over the years. In this paper, we present a novel pedestrian detection approach based on supervised classification. We propose here the use of basic statistical operators to adapt support vector regression (SVR) to binary classification...
In our study we present a method to identity pathological voices using Support Vector Machines (SVM). Speech signals were sampled from the sustained vowel /a/ pronounced by 160 subjects (80 female and 80 male), including 80 speakers (40 women and 40 men) suffered from various dysphonias (such as acute laryngitis, adductor spasmodic, vocal fatigue, vocal tremor, vocal fold edema, laryngeal paralysis…),...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.