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Hyperspectral sensors acquire a set of images from hundreds of narrow and contiguous bands of electromagnetic spectrum from visible to infrared regions. The computational complexity is very high for classification of hyperspectral images due to the presence of large number of bands. In such a scenario, feature selection is very essential technique for reducing the dimensionality. In the proposed work,...
In this present work, a technique for differentiation of normal and cirrhotic liver segmented regions of interest (SROIs) based on Laws' masks analysis is reported. Thirty four B-mode ultrasound images taken from 22 normal volunteers and 12 patients suffering from liver cirrhosis were collected from Department of Radiodiagnosis and Imaging, PGIMER, Chandigarh, India. The filtered texture images are...
Detection and elimination of the shadows of moving objects in video sequences have been one of the major challenges in tracking applications. Since moving shadows can't be removed from foreground by background subtraction methods, they lead to confusion and error in moving object tracking. In this paper, we propose a novel classification method based on hierarchical mixture of experts learning for...
Epilepsy is the most common neurological disorder, affecting between 0.6% and 0.8% of the global population. During an epileptic seizure, the onset of which tends to be sudden and without prior warning, sufferers are highly vulnerable to harm, and methods that might accurately predict seizure episodes in advance are clearly of value. Building on recent work by Costa et al, we compare and contrast...
In this paper, we suggest a new set of statistic feature for the electroencephalogram (EEG) signals classification. We use two methods of seizure detection for evaluate new of statistic feature. Initially, features are extracted from EEG signals by using discrete wavelet transform. Next, a set of statistical features are extracted from each frequency sub-band to represent the distribution of wavelet...
In computational anatomy, statistical shape model (SSM) is used for the quantitative evaluation of variations in the shapes of different organs. This paper focuses on the construction of a SSM of the liver and its application to computer-assisted diagnosis of cirrhosis. We prove the potential application of SSMs in the classification of normal and cirrhotic livers. In constructing a SSM of the liver,...
This paper presents a design for a classifier using detection and classification of eight daily movements data collected with two tri-axial accelerometers, one mounted on the right part of the hip and the other one mounted on the lower part of the right leg. This classifier gave good accuracy of 99.8% in controlled laboratory experiments, in which four healthy subjects carried out a set of eight basic...
This paper proposes pedestrians' attribute analysis such as gender and whether they have bags with them based on multi-layer classification. One of the technically challenging issues is we use only top-view camera images to protect the privacy of the pedestrians. The shape features over the frames are extracted by bag-of-features (BoF) using histogram of oriented gradients (HoG) vectors with the optimized...
Acoustic Emission signal reflecting the tool wear state is made by phase space reconstruction that uses mutual information method and Cao method to determine time delay and embedding dimension for constructing phase space matrix. After reconstruction, by calculating singular spectral of phase space matrix, based on which characteristic vector is constructed. These characteristic vectors are combined...
In this article, a real-time, accurate and objective identification of different varieties of corn seeds is proposed, which is a large number of original features, contained color, texture and shape features, were extracted from corn seed images. Then, genetic algorithm and support vector machine (SVM) were used to select important ones and determine species. The proposed methods have optimized varieties...
Traditional classification algorithms used in remote sensing images have many problems, such as the low operation speed, low accuracy and difficult convergence. Support Vector Machine (SVM) is a new machine learning method of statistical learning theory based on small samples of machine learning rules. This paper deals with the remote sensing image classification by the support vector machine, using...
A previously developed neural-machine interface (NMI) based on neuromuscular-mechanical fusion has showed promise for recognizing user locomotion modes; however, errors of NMI during mode transitions were observed, which may challenge its real application. This study aimed to investigate whether or not the prior knowledge of walking environment could further improve the NMI performance. Linear Discriminant...
Majority of the recently developed brain computer interface (BCI) systems have been using visual stimuli or visual feedbacks. However, the BCI paradigms based on visual perception might not be applicable to severe locked-in patients who have lost their ability to control their eye movement or even their vision. In the present study, we investigated the feasibility of a vision-free BCI paradigm based...
The classification of multisensor data sets, consisting of multitemporal SAR data and multispectral is addressed. In the present study, Import Vector Machines (IVM) are applied on two data sets, consisting of (i) Envisat ASAR/ERS-2 SAR data and a Landsat 5 TM scene, and (ii) TerraSAR-X data and a RapidEye scene. The performance of IVM for classifying multisensor data is evaluated and the method is...
Relevance Vector Machine (RVM) technique as a new machine learning method is illustrated in details. It is a novel kind of learning method which is based on Bayesian learning theory. RVM presents the good generalization performance, and its predictions are probabilistic. Relevance vector machine mathematics model doesn't have regularization coefficient and its kernel functions do not need to satisfy...
Semi-supervised learning has attracted a lot of attention in recent years. Different from traditional supervised learning. Semi-supervised learning makes use of both labeled and unlabeled samples. In text categorization, traditional classifier prefer lots of samples and each category have same number of simples, but collecting labeled examples costs human efforts and certain category may be can't...
Web services run in a highly dynamic environment (the Internet) which makes the composite service will face multiple exceptions in its execution. Thus, it needs to take effective actions to deal with the errors causing the exceptions. Then, by such actions, composite service can adapt to the dynamics and complexity of its execution environment. Since accurately identifying the error source which causes...
A layout recognition method for multi-page document image is proposed in this paper. Because there exists of spacings in vertical and horizontal direction in this kind of document, vertical and horizontal projection are used to extract the layout feature and Naive Bayes classifier is generated to realize the layout recognition of multi-page document. Experimental results show that the method of this...
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