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Local Binary Pattern (LBP) and its variants are effective and popular descriptors for texture classification. Most LBP like descriptors have disadvantages including sensitiveness to noise and inability to capture long distance texture information. In this paper we propose a simple, efficient, yet robust multi-resolution descriptor to texture classification — Binary Rotation Invariant and Noise Tolerant...
This paper presents the Intrinsic Mode Function Time Delay Method (IMFTDM) which is based on Hilbert Huang Transform (HHT) and its application in beamforming. The IMFTDM can delay the broadband Intrinsic Mode Function (IMF) by arbitrary time accurately. And in this work, the IMFTDM is performed using the simulated and real data in beamforming to test its performance. The results show that it can improve...
Multi-temporal remote sensing data can provide much more information that could be used to improve the accuracy of classification of vegetation types. However, it is always required to manually select a set of training samples by using conventional supervised classification methods, which is a time-consuming and costly task. In this paper, a new classification method based on spectral knowledge database...
In order to implement the remote sensing applied in land use research, we use a series of remote sensing image processing commercial software to extract land use/land cover information. The article are based on compare and analyze the ability of identify information of land use and land cover between two software (Erdas imagine 8.5 and ENVI4.1) which using the same classification method in a imagine...
In the environment with objects moving randomly, the positions of moving objects can be modeled as a range of possible values, associated with a probability density function. Data mining of such positions of uncertain moving objects attracts more and more research interest recently. The definitions of probabilistic core object and probabilistic density-reachability are presented and a density-based...
Support vector machine (SVM) has been widely applied in the classification of remotely sensed image. How to reduce support vector number in SVM classifier so as to reduce classification time still an important open problem, especially in the case of mass data. To obtain fast classifier with high accuracy, an active learning schema is proposed in the SVM based image classification. Experimental results...
In many applications of wireless sensor networks, location is very important data. Location data can come from manual setting or GPS device. However, manual setting requires huge cost of human time, and GPS setting requires expensive device cost. Both approaches are not applicable to localization task of large scale wireless sensor networks. In this paper, we propose an accurate and efficient localization...
Image registration based on the features of an image is a major research direction. It is common sense that there are many problems in traditional feature registration including a large amount of computations, high complexity and low accuracy. So, an auto image registration method based on natural characteristic points and global homography is proposed in this paper. In this method, firstly, the feature...
Support Vector Machines (SVM) is characteristic of processing complex data and high accuracy. An ensemble of classifiers often results in better performance than any single classifier in the ensemble. In this paper, bagging, boosting, multiple SVM decision model (MSDM) and heterogeneous SVM decision model (HSDM) of SVM ensemble are compared on four data sets. For boosting, a novel strategy for weight...
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