Aiming at the characters of weak and small targets in infrared images, an algorithm based on Least Squares Support Vector Machines (LS-SVM) is presented to fuse long-wave and mid-wave infrared images and detect target...
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ISBN:
(纸本)9780819469601
Aiming at the characters of weak and small targets in infrared images, an algorithm based on Least Squares Support Vector Machines (LS-SVM) is presented to fuse long-wave and mid-wave infrared images and detect targets. image intensity surfaces for the neighborhood of every pixel of the original long-wave infrared image and mid-wave infrared are well-fitted by mapped LS-SVM respectively. And long-wave and mid-wave infrared image gradient images are obtained by LS-SVM based on radial basis kernels function. Fusion rule is set up according to the features of gradient images. At last, segment fused image and targets can be detected with contrast threshold. Compared with wavelet fusion detection algorithm and morphological fusion detection algorithm, when a target is affected by baits, the experimental results demonstrate that the proposed approach in the paper based on LS-SVM to fuse and detect weak and small target is reliable and efficient.
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...
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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.
Wide baseline stereo correspondence has become a challenging and attractive problem in computer vision and its related applications. Getting high correct ratio initial matches is a very important step of general wide ...
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ISBN:
(纸本)9780819469526
Wide baseline stereo correspondence has become a challenging and attractive problem in computer vision and its related applications. Getting high correct ratio initial matches is a very important step of general wide baseline stereo correspondence algorithm. Ferrari et al. suggested a voting scheme called topological filter in [3] to discard mismatches from initial matches, but they didn't give theoretical analysis of their method. Furthermore, the parameter of their scheme was uncertain. In this paper, we improved Ferraris' method based on our theoretical analysis, and presented a novel scheme called topologically clustering to discard mismatches. The proposed method has been tested using many famous wide baseline image pairs and the experimental results showed that the developed method can efficiently extract high correct ratio matches from low correct ratio initial matches for wide baseline image pairs.
A satisfied deformable object simulation should be general, accurate, efficient and stable. Explicit, implicit and semi-implicit numerical integration methods have contributed to large performance enhancements in the ...
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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...
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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.
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...
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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.
Instead of traditionally using a 3D physical model with many control points on it, a calibration plate with printed chess grid and movable along its normal direction is implemented to provide large area 3D control poi...
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Instead of traditionally using a 3D physical model with many control points on it, a calibration plate with printed chess grid and movable along its normal direction is implemented to provide large area 3D control points with variable Z values. Experiments show that the approach presented is effective for reconstructing 3D color objects in computer vision system.
Motivated by the requirements of archaeologists we are developing an automated system for acquisition, documentation and management of daily finds of excavations. These daily finds can be separated into large objects ...
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Motivated by the requirements of archaeologists we are developing an automated system for acquisition, documentation and management of daily finds of excavations. These daily finds can be separated into large objects like remainders of architecture and small objects of ancient daily life - like ceramics and coins. Ceramics especially are found in numbers of tens of thousands on virtually every excavation, because ceramics have been in use for thousands of years. Until the present day these finds are documented by manual drawings. There is a similar situation in the case of coins, where manual drawings are often used to abstract them from photographs. Therefore we propose an automated acquisition and documentation system based on digital cameras and structured light for small findings. For ceramics we provide further processing to estimate horizontal cross-sections (profile-lines) for printed documentation, as it is done by manual drawing. For this a proper orientation of the acquired 3D-model is required and automatically estimated based on the assumption that ceramics were made on rotational plates (wheels). We are aware that ceramics might not always have been manufactured on rotational plates, because the wheel was not invented everywhere as for the example in the Americas. Even though ceramics from such areas appear to be rotational symmetric, we developed a method based on shape and symmetry analysis to determine the manufacturing techniques of ceramics. This helps to answer another archaeological question regarding the technological advance of an ancient culture. Results for accuracy and performance are shown on real data from recent interdisciplinary projects together with archaeologists from Austria, Germany, Israel and Peru. Furthermore we present preliminary results of the integration of coin classification in our documentation system. Additionally we are currently adapting the London Charter to ensure the intellectual integrity, reliability, transparency,
This paper introduces a new class of switching vector median filter. The proposed algorithm first uses four directional masks to analyze the color difference between the central pixel and its neighborhood pixels in th...
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ISBN:
(纸本)9780819469502
This paper introduces a new class of switching vector median filter. The proposed algorithm first uses four directional masks to analyze the color difference between the central pixel and its neighborhood pixels in the RGB color space and classify each color pixel into noisy pixel or noise-free one, and then employs the standard vector median filtering operations in the detected noisy locations to restore the corrupted pixels and leave the noise-free ones unchanged. The simulation results show that the proposed method excellently suppresses impulsive noise as well as preserving the image details well, and significantly outperforms the existing vector filtering solutions in terms of both the objective measures and the perceptual visual quality.
In face recognition, the dimensionality of raw data is very high, dimension reduction (feature extraction) should be applied before classification. There exist several feature extraction methods, commonly used are pri...
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In face recognition, the dimensionality of raw data is very high, dimension reduction (feature extraction) should be applied before classification. There exist several feature extraction methods, commonly used are principle component analysis (PCA) and linear discriminant analysis (LDA) techniques. In this paper, we present a comparative study of some feature extraction methods for face recognition in the same conditions. The methods evaluated here include eigenfaces, kernel principal component analysis (KPCA), fisherfaces, direct linear discriminant analysis (D-LDA), regularized linear discriminant analysis (R-LDA), and kernel direct discriminant analysis (KDDA). For the purpose of comparison on feature extraction methods, we adopt nearest neighbor (NN) algorithm from existed classifiers of face recognition, since this classifier is common and simpleness. Empirical studies are conducted to evaluate these feature extraction methods with images from ORL Face Database, and it is found that in most cases LDA-based methods are efficient than PCA-based ones.
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