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Aging population and empty-nesters are two big challenges in modern healthcare. Fall incidents can cause various physical injuries and serious consequence without receiving timely assistance. Therefore, fall detection and movement classification have very high research value and application significance. This paper aims to study the optimum feature subset of falls and put forward an improved approach...
According to the characteristics of infrared images of electrical equipment, a new image segmentation method based on wavelet transformation and fuzzy clustering is proposed in this paper. Because image segmentation based on the fuzzy C mean (FCM) clustering algorithm is easy to be affected by the initial clustering center and the clustering number, which often leads to the convergence of results...
Aging population is a big challenge in modern healthcare. Injuries caused by fall incidents are great threats to the elderly. As a consequence, fall detection and movement classification have very high research value and application significance. This paper aims to study the optimum feature subset of falls and verify the effectiveness of the proposed features through experiments. A set of twelve features...
In the Brain-computer interface, classification and recognition technology plays an important role, especially the EEG classification and recognition for the movement imagery. In this paper, we use a new type of sensors to collect EEG signals. According to imagine the movement of left or right hand to identify two types of thinking, we proposed a new recognition method based on AR(auto-regressive)...
Fuzzy clustering algorithms have been successfully applied to POLSAR classification, but not to POLInSAR. In this paper, a Fuzzy C Means (FCM) clustering algorithm integrating the complementary physical information and statistical property contained in both polarimetric and interferometric data, is used for POLInSAR classification. At first, the area dominated by volume scattering is extracted from...
A wide variety of present approaches work well in detecting frontal faces, but they are often unable to detect faces with partial occlusion, rotation and strong shadows. To address this problem, we propose an efficient technique. First, we filter the face-like regions from the input image using skin-color model. And then component classifiers are used to detect the faces' components from these potential...
Along with the rapid development of network technology, the application of internet is wider. The network service also presents the characteristics of diversification. Thus the traditional real-time services have not already people's need. Immediately what to produce is various multi-media businesses such as, video, audio, the television meeting etc. with high request of real time. The traditional...
Currently, condition-based maintenance becomes more and more important with the addition of factory automation through the development of new technologies. For many complicated machines, it is difficult to use the mathematical model to describe their faults. Intelligent maintenance makes it possible to perform maintenance similar to that of a human being. Support vector machine (SVM) has become famous...
3D moment invariants are traditionally based on region characteristics and the location of every pixel point, this will cause high calculation cost. In this paper, a novel shape representation named 3D gray level moment invariants is constructed. Some properties of the new representation including the independence of the translation, scaling and rotation transforms are proved. Experiments indicate...
It's a hotspot to expend the research on support vector machine from a two-class issue to a multi-class one. Among all kinds of methods, Bintree multi-class text categorization algorithm based on support vector machine is more effective in training and sorting then others, and it works out the impartibility problem. So it is a good method. The dissertation systematically researches and analyses Bintree...
Fuzzy c-means (FCM) clustering algorithm has been widely used in automated image segmentation. However, the standard FCM algorithm takes a long time to partition a large dataset. In addition, in current fuzzy cluster algorithms it is difficult to determine the cluster centers. This paper proposes a modified FCM algorithm for MR (magnetic resonance) brain images segmentation. This method fetches in...
The main indicators of the performance of an image classifier include classification accuracy, classification efficiency and evolution efficiency, whereas the sizes of the programs involved in genetic programming stand as one of the major factors that influence the performance of the classifier. Some effective means are introduced in this paper for the control of program sizes, which is done through...
During IC photomask vision inspection, considering problem that fine image defectpsilas fineness, complex shape, extraction feature difficultly, and effect by noise easily, presented defect identification classification algorithm based on PCA (principal components analysis) and SVM (support vector machine). It resolved the problem that fine and complex defect was difficult to classify, by merits of...
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