The sliding window method will cause the severe unbalanced dataset problem. In this paper, under-sample the majority class method is adopted to solve this problem, and SVM is used to classify the processed data. The b...
详细信息
Curved screens are often used in virtual reality vision systems. But distortion happens when projecting on a curved surface. Some special projectors and equipment have been invented to solve this problem. Instead of u...
详细信息
Under some special conditions, the P3P problem can have 1, 2, 3 and 4 solutions, and if the 3 control points and the optical center lie on a circle, the problem is indeterminate. In this paper, by the Monte Carlo appr...
详细信息
Under some special conditions, the P3P problem can have 1, 2, 3 and 4 solutions, and if the 3 control points and the optical center lie on a circle, the problem is indeterminate. In this paper, by the Monte Carlo approach of up to 1 million samples, it is shown that the probabilities of the P3P problem with one solution, two solutions, three solutions, and four solutions are respectively 0.9993, 0.0007, 0.0000, 0.0000. The result confirms the well-known fact that in the most cases, the P3P has a unique solution.
Classification of multi-source remote sensing images has been studied for decades, and many methods have been proposed or improved. Most of these studies focus on how to improve the classifiers in order to obtain high...
详细信息
Classification of multi-source remote sensing images has been studied for decades, and many methods have been proposed or improved. Most of these studies focus on how to improve the classifiers in order to obtain higher classification accuracy. However, as we know, even if the most promising method such as neural network, its performance not only depends on the classifier itself, but also has relation with the training pattern (i.e. features). On consideration of this aspect, we propose an approach to feature selection and classification of multi-source remote sensing images based on Mallat fusion and residual error in this paper. Firstly, the fusion of multi-source images can provide a fused image which is more preferable for classification. And then a featureselection scheme approach based on fused image is proposed, which is to select effective subsets of features as inputs of a classifier by taking into account the residual error associated with each land-cover class. In addition, a classification technique base on selected features by using a feed-forward neural network is investigated. The results of computer experiments carried out on a multisource data set confirm the validity of the proposed approach.
The software systems which are related to national science and technology projects are very crucial. This kind of systems always involves high technical factors and has to spend a large amount of money, so the quality...
详细信息
The software systems which are related to national science and technology projects are very crucial. This kind of systems always involves high technical factors and has to spend a large amount of money, so the quality and reliability of the software deserve to be further studied. Hence, we propose to apply four intelligent classification techniques most used in data mining fields, including Bayesian belief networks (BBN), nearest neighbor (NN), rough set (RS) and decision tree (DT), to validate the usefulness of software metrics for risk prediction. Results show that comparing with metrics such as Lines of code (LOC) and Cyclomatic complexity (V(G)) which are traditionally used for risk prediction, Halstead program difficulty (D), Number of executable statements (EXEC) and Halstead program volume (V) are the more effective metrics as risk predictors. By analyzing obtained results we also found that BBN was more effective than the other three methods in risk prediction.
In this paper, we present a novel approach towards 3D shape recognition and retrieval using histograms of rotation invariant local features. Features are extracted for every point of voxelized 3D shape objects by use ...
详细信息
ISBN:
(纸本)9784901122078
In this paper, we present a novel approach towards 3D shape recognition and retrieval using histograms of rotation invariant local features. Features are extracted for every point of voxelized 3D shape objects by use of functions on spheres which are invariant towards rotation of the object. The fast computation of the local features is performed via convolution methods in frequency space. Histograms of these features describe an object in terms of distributions of local geometric properties such as orientation and angle of edges, distances and convexity. Object classification is performed by Support Vector Machines with histogram-intersection kernels. In experiments on the Princeton Shape Benchmark [1], our approach outperformed many existing methods in several classification and retrieval tasks.
A novel video scrambling algorithm based on generalized Fibonacci numbers is given in this paper. The experiment results show that the algorithm has better robust than the traditional video scrambling algorithm. Based...
详细信息
ISBN:
(纸本)9780889867024
A novel video scrambling algorithm based on generalized Fibonacci numbers is given in this paper. The experiment results show that the algorithm has better robust than the traditional video scrambling algorithm. Based on the property of uniformity of the corresponding Fibonacci transformation, the suggested method has the following advantages: (1) Encoding and decoding are very simple and they can be applied in real-time situations. (2) The algorithm can endure severe attacks such as extreme noise levels, very high loss in its data or data packets. (3) The algorithm is independent on any video format or encode mode.
The Based on the background of anti counterfeit of commercial bills, a novel digital watermarking method is proposed in this paper. The watermarking algorithm is based on a class of orthogonal function systems - V sys...
详细信息
Two-dimensional principal component analysis (2DPCA) for face recognition has been proposed which is based on 2D matrices. It needs more coefficients for feature vectors than principal component analysis (PCA). In thi...
详细信息
Huge image databases require the automatic analysis of image content in order to retrieve information. Especially the detection and localization of visual object class members is an important issue. In this work, we d...
详细信息
ISBN:
(纸本)9784901122078
Huge image databases require the automatic analysis of image content in order to retrieve information. Especially the detection and localization of visual object class members is an important issue. In this work, we deal with the localization of visual object class members in a patch based object recognition framework. In particular, we show how not only the location and scale of an object can be determined, but also the orientation, a parameter typically neglected in current localization systems. Our method uses features computed at Difference of Gaussian interest points and remembers the orientation of the local patches relative to the reference object. Using a general Hough transform like voting scheme, the position and orientation of query objects can be retrieved. Tests on two different leaf databases show the capabilities of the approach.
暂无评论