Past decades, numerous spectral analysis based algorithms have been proposed for dimensionality reduction, which plays an important role in machine learning and artificial intelligence. However, most of these existing...
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Past decades, numerous spectral analysis based algorithms have been proposed for dimensionality reduction, which plays an important role in machine learning and artificial intelligence. However, most of these existing algorithms are developed intuitively and pragmatically, i.e., on the base of the experience and knowledge of experts for their own purposes. Therefore, it will be more informative to provide some a systematic framework for understanding the common properties and intrinsic differences in the algorithms. In this paper, we propose such a framework, i.e., ldquopatch alignmentrdquo, which consists of two stages: part optimization and whole alignment. With the proposed framework, various algorithms including the conventional linear algorithms and the manifold learning algorithms are reformulated into a unified form, which gives us some new understandings on these algorithms.
This paper describes a novel 3D needle segmentation algorithm for 3DUS data. The algorithm includes the 3D Gray-level Hough Transform (3DGHT), which is based on the representation (ψ, θ, ρ, α) of straight lines in...
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A quick 3D needle segmentation algorithm for 3D US data is described in this paper. The algorithm includes the 3D quick randomized Hough transform (3DGHT), which is based on the 3D randomized Hough transform and coars...
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A quick 3D needle segmentation algorithm for 3D US data is described in this paper. The algorithm includes the 3D quick randomized Hough transform (3DGHT), which is based on the 3D randomized Hough transform and coarse-fine searching strategy. We tested it with water phantom. The results show that our algorithm works well in 3D US images with angular deviation less than 1 degree and position deviation less than 1 mm, and the computational time of segmentation with 35 MB data is within 1s.
To infrared images, the contrast of target and background is low, dim small targets have no concrete shapes and their textures cannot be reliable predicted. The paper puts forward a novel algorithm to fuse mid-wave an...
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To infrared images, the contrast of target and background is low, dim small targets have no concrete shapes and their textures cannot be reliable predicted. The paper puts forward a novel algorithm to fuse mid-wave and long-wave infrared images and detect targets. Firstly, the source images are decomposed by wavelet transformation. In usual, targets in infrared images are man-made, and their fractal dimension is different comparing with natural background. In wavelet transformation domain high-frequency part, we calculate local fractal dimension and set up fusion rule to merge corresponding sub-images of two matching source images. In low-frequency, we extract local maximum gray level to fuse them. Then reconstruct image by wavelet inverse transformation and obtain fused result image. In fusion results, the contrast between targets and background has obvious changes. And targets can be detected using contrast threshold. The experimental results show that the method proposed in this paper using wavelet transformation fractal dimension to fuse dual band infrared images, and then detect targets is better than using mid-wave or long -wave infrared images detect targets alone.
ServiceBSP model is presented as an extension of BSP model with a view to the advantages of BSP model in Grid environment where large-scale and geographically distributed resources (abstracted as services) are availab...
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Automated tongue image segmentation in tongue diagnosis system of traditional Chinese medicine is difficult due to two factors: There are lots of pathological details on the surface of tongue, and the shapes of tongue...
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In this paper, a novel neural network based manifold learning method(NNBML)[1] recently appeared in the Journal of science is introduced. It can effectively convert high-dimensional data into low-dimensional codes, wh...
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ISBN:
(纸本)9781601320438
In this paper, a novel neural network based manifold learning method(NNBML)[1] recently appeared in the Journal of science is introduced. It can effectively convert high-dimensional data into low-dimensional codes, which are then used for classification. However, it performs not well while dealing with small size face database used for face recognition. We propose a solution generating more samples data based on the existing data. The proposed method is implemented on two well-known face databases, viz. ORL and Yale face databases. The experimental results show that NNBML is able to deal with the task of face recognition after more data samples generated using the proposed method, and also that NNBML outperforms LDA in terms of recognition rate.
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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This paper presents uncertainty propagation in landmark based position estimation methods. Analysis of two methods has been carried out where robot position is estimated by detecting one or two globally distinct featu...
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ISBN:
(纸本)9784901122078
This paper presents uncertainty propagation in landmark based position estimation methods. Analysis of two methods has been carried out where robot position is estimated by detecting one or two globally distinct features using a pivoted stereo vision system. We make a basic assumption about error in estimating point features in camera images and propagate it into robot position estimate using first order approximation of non-linear functions. Simulation results illustrate the performance of the method.
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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