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Haze and mist always affect the quality of vision. If an image is suffered from haze or mist, then the object is unclear and the image seems whiter than the original one. There are several haze removal algorithms that can reduce the effect of haze and mist. However, if an image is not suffered from the haze and mist, applying the haze removal algorithm may darken the image. Therefore, in computer...
Image feature extraction is a challenging task as it directly affects analysis of Satellite Image Time Series (SITS) which tackles a huge amount of information (spatial and spectral resolution increase). Therefore, in this paper, Principle Component Analysis (PCA) is applied for feature extraction to improve a multitemporal classification approach for Very High Resolution (VHR) SITS. The improved...
The development of robust object-oriented classification approaches suitable for medium to high spatial resolution satellite imagery provides a valid alternative to traditional pixel-based classification approaches. In the past, Support Vector Machines (SVM) have been tested and evaluated only as pixel-based image classifiers. Moving from pixel-based analysis to object-based analysis, a fuzzy classification...
Underwater target classification is a very demanding task owing to ever changing complicated nature of the underwater communication channels. Underwater target classification system identifies targets from a mixture of underwater events by its characteristic signature. The characteristic signatures pertaining to each target are patterned by feature recognition algorithms operating on hydrophone captured...
Chronic kidney disease is a universal common obstacle which its outcomes can be prevented or delayed by early detection and cure. Classification of kidney disease is vital for global improvement and accomplishment of practical guidance. Therefore, data mining and machine learning techniques can be used to discover knowledge and identify patterns for classification. Since there exist features that...
Breast cancer has caused more and more attention in recent years since the mortality rate is increasing and age of onset is trend to be younger than before. Using computer vision technology for automatic classifying benign and masses malignant ones could assist doctors in diagnosing condition. However, the margins and shapes of masses are various and which are very similar with surrounding tissues,...
Movement classification has been a challenging problem in neuroprosthesis control. Many studies have taken into account the classification of movement using time and frequency domain features extracted from the electromyogram signals while calculating these features are usually time consuming. In this paper, we compared the capability of muscle activation waveform in the classification of five arm...
This paper aims to highlight the performances and advantages of three improved and fast AI algorithms that are mainly used in classification problems suitable for various fields. The discussions regarding the benchmark results appeal to the Modified version of Radial Basis Function (RBF-M) mentioned in the paper as Fast Support Vector Classifier (FSVC) or Fast Support Vector Machine, Extreme Learning...
This paper proposes an efficient tracking method to handle the appearance of object. Distribution fields descriptor (DF) which allows the representation of uncertainty about the tracked object has been proved to be very robust to illumination changes, image noise and small misalignments. However, DF tracking is a generative model that does not utilize the background information, which limits its discriminative...
Domain adaptation methods show better ability to learn when the training data is not identically and independently distributed. The key task of domain adaptation is to find a suitable measure to scale the distributed difference between source domain and target domain. So a projected maximum divergence discrepancy distance measure is proposed. Based on the structural risk minimization theory and the...
Network traffic classification plays an extremely important role in network management and service. Support vector machine (SVM) is widely adopted to classify traffic flows for its high accuracy. All features selected are treated equally in traditional SVM network traffic classification, which take little consideration of that each feature exerts a different influence on classification. Therefore,...
Analysis of safety inventory decision is of great significance to effectively reduce the inventory cost and fund occupancy rate, and to ensure timely material supply of power grid, while analysis of safety inventory decision of power companies is based on material consumption forecasting data. As the industry particularity of power company material consumption, the existing problems of data are not...
Machine learning can play a very important role in various crucial applications like data mining and pattern recognition. Machine learning techniques have been widely used in drug discovery and development, particularly in the areas of chemo-informatics, bioinformatics and other types of pharmaceutical research. It has been demonstrated that they are suitable for large high dimensional data, and the...
This paper presents a novel multikernel based Sparse representation for the classification of Remotely sensed images. The sparse representation based feature extraction are in a run which is a signal dependent feature extraction and thus more accurate. Multikernel Sparse representation was also had proved to be more accurate and less computationally complex while implemented in other applications...
Motion deblurring is a knotty problem, especially when the photo is shot in a low lighting scene with a long exposure time or a strong reflected lighting scene. In this situation, partially saturated pixels violate the assumption of linear model and the recovered image contains severe ringing artifacts. In this paper, we proposed a novel two-layer images independent deconvolution method that redefine...
In this paper, autoregressive (AR) model coefficients and support vector machine (SVM) are used to classify the motor imagery EEG available from the well-known BCI competition database. In order to determine AR order, we use paired t-test to assess the impact of AR order on the classification precision of motor imagery EEG. The results show that there is a significant difference in the classification...
Auscultation, a method to detect the condition of heart by examining the heart sounds, is widely used by cardiologists. Using artificial intelligence methods in auscultation to detect various heart diseases is increasing in present days. In this paper, we try to classify 5 different categories of mechanical artificial heart valve sounds. Considering that such classification task is highly nonlinear,...
Kernel target alignment is a very efficient evaluation criterion. It has been widely applied in kernel optimization. However the traditional kernel methods that based on the Kernel target alignment optimize the kernel function mainly with batch gradient descent algorithm. This kind of methods has to scan through the entire training set at each step, which is much too costly. The On-line learning algorithm...
The large-margin principle has been widely applied to learn classifiers with good generalization power. While tremendous efforts have been devoted to develop machine learning techniques that maximize margin quantity, little attention has been paid to ensure the margin quality. In this paper, we proposed a new framework that aims to achieve superior generalizability by considering not only margin quantity...
In this paper, a novel unsupervised method for learning sparse features combined with support vector machines for classification is proposed. The classical SVM method has restrictions on the large-scale applications. This model uses sparse auto encoder, a deep learning algorithm, to improve the performance. Firstly, we use multiple layers of sparse auto encoder to learn the features of the data. Secondly,...
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