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.
The high spectral and spatial resolution of hyperspectral images increases the capability to distinguish physical materials and objects, presenting new challenges to image analysis and classification. In fact, many studies have been conducted to extract and integrate spectral and contextual information in the classification process. However, the availability of various spatial features (e.g. morphological...
In recent years, the design of classification algorithms, with the aid of information combination methods, has received a considerable attention. In machine vision, in order to overcome the high inter-class variations between the classes of image, various feature descriptors have been designed to be robust to these inter-class variations. However, no single feature can be robust to these variations...
Finding chemical compounds that can be used to treat a certain disease has long been a focus of the biomedical research. Using traditional laboratory approaches, scientists have to test numerous chemical compounds in order to find a drugable compound. Computational methods can speed up this screening process. We compared various computational methods that predict the function of chemical compounds...
This paper proposes a novel approach for automatic estimation of four important traits of speakers, namely age, height, weight and smoking habit, from speech signals. In this method, each utterance is modeled using the i-vector framework which is based on the factor analysis on Gaussian Mixture Model (GMM) mean supervectors, and the Non-negative Factor Analysis (NFA) framework which is based on a...
A texture descriptor based on a set of indices of degrees of local approximating polynomials is proposed in this paper. An image is split into non-overlapping patches, reshaped into one-dimensional source vectors and convolved with the polynomial approximation kernels of various degrees p. As a result, a set of approximations is obtained. For each element of the source vector, these approximations...
In this paper an object-based method for multispectral image segmentation and classification is proposed. Normally, in remote sensing a scene is represented by pixel-based features. It is possible to reduce data redundancy by a segmentfeature extraction process, where the segment-features, rather than the pixel-features, are used for multispectral scene representation and classification. Object-based...
This paper presents a new method for discriminating centroblast (CB) from non-centroblast cells in microscopic images acquired from tissue biopsies of follicular lymphoma. In the proposed method tissue sections are sliced at a low thickness level, around 1–1.5um, which provides a more detailed depiction of the nuclei and other textural information of cells usually not distinguishable in thicker specimens,...
Reliable and fast discrimination between internal faults and inrush conditions is still a challenging issue. In this paper an application of Support Vector Machine (SVM) for the transformer differential protection is discussed. To achieve the satisfactory classification strength various input vectors and training parameters were considered. Finally, 16 different versions of SVM classifiers are proposed...
A kernel-based approach is proposed in this paper to address supervised classification of polarimetric SAR data. Relevant features extracted from such data are generally complex-valued (e.g., scattering coefficients, multilook covariance-matrix entries). First, based on the theory of complex reproducing kernel Hilbert spaces (RKHS's), a family of admissible kernel functions tailored to the classification...
Classification of stress is imperative especially with regard to automobile drivers since stress level of the driver forms a major factor for accidents. This paper deciphered the classification of stress of automobile drivers using Radial Basis Function Kernel Support Vector Machine (SVM) classifier. The nonlinear separation of features in feature space was deciphered by this kernel trick. Pertinent...
Environmental monitoring is one of the key approaches to safeguard the global ecosystem. Classifications of different water levels facilitate in preserving water reserves and maintain the equilibrium in the ecosystem. In this paper we shall inspect the classification of drainage water levels in Canada. A powerful statistical tool called support vector machines is used to classify the said drainage...
One of the most important abilities of human is cluster and classify similar things, which makes people could better understand the nature, easier establish and manage the social society. How to model things like people and how to compute the similarities between models are two major problems need to be solved to make the machine has this ability. For the first problem, the Semantic Link Network (SLN)...
In this paper, a kind of clipping detection method for audio signal is proposed based on kernel Fisher discriminant (KFD) in MDCT domain. The kernel method and the Fisher linear discriminant analysis (FLDA) are introduced to the proposed method. First, the clipping and non-clipping feature parameters are extracted using MDCT coefficients of audio signals. Next, the optimal projection vector and the...
A phoneme recognition system based on Discrete Wavelet Transforms (DWT) and Support Vector Machines (SVMs), is designed for multi-speaker continuous speech environments. Phonemes are divided into frames, and the DWTs are adopted, to obtain fixed dimensional feature vectors. For the multiclass SVM, the One-against-one method with the RBF kernel was implemented. To further improve the accuracies obtained,...
In this paper, we consider the image classification problem. Unlike conventional local learning technique, a novel framework, which is based on the proposed sparsity induced neighbors (SINs) instead of widely used k nearest neighbors, is presented. Within this framework, the SINs of test image are training images associated with the nonzero entries in the sparse representation of test image, and they...
Handwritten signatures are one of the most widely used biometrics, particularly in financial and legal transactions. Offline Signature verification is still one of the most challenging problems in biometrics. In this study, we have evaluated the performance of different classifiers for offline signature verification based upon local binary patterns feature set. The feature vector is formed by dividing...
Support vector machine approach is an effective technique to solve poly-dimensional outlier detection, which can avoid the curse of dimensionality problem and has higher accuracy. One-class support vector machine-based outlier detection techniques take advantage of spatial and temporal correlations that exist between sensor data to cooperatively identify outliers. However, for large scale training...
In the text-independent speaker recognition system, Support Vector Machine (SVM) equipped with sequence kernel has been widely used. In this paper, a generic structure conceiving sequence kernel has been encapsulated and in the structure we make an analytical comparison between two well used sequence kernel system-GMM Super vector Kernel (GSK) and Generalized Linear Discriminant Sequence (GLDS) showing...
Several studies explored the application of Discriminant analysis on Grassmann manifolds to tackle the image sets matching. But these methods suffer from not considering the local structure of the data. In this paper, a new method of face recognition which based on a graph embedding framework and geometric distance perturbation has been proposed. By introducing similarity graphs and maximal linear...
Population of old generation that live alone is growing in most countries. Surveillance systems help them stay home and reduce the burden on the healthcare system. Automatic visual surveillance systems have advantages over wearable devices. They extract features from video sequences and use them for event classification. But these features are dependent on the position of cameras relative to the person...
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.