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Color image superresolution using multi-channel data fusion

Color image superresolution using multi-channel data fusion

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3rd international symposium on multispectral image processing and pattern recognition

作者： Zhao, SB Han, H Peng, SL Chinese Acad Sci Inst Automat Beijing 100080 Peoples R China

ISBN: (纸本)0819451819

This paper presents a wavelet-domain Hidden Markov Tree(HMT)-based color image superresolution algorithm using multi-channel data fusion. Because there exists correlations among the three channels of a RGB color image, a channel by channel superresolution method almost certainly leads to color distortions. In order to solve this problem, first the low-resolution color image is converted into a gray-scale image using the spatially-adaptive approach presented in this paper and the resulting gray-scale image must reflect the human perception of edges in the color image;then by superresolving this gray-scale image, a high-resolution image is obtained;finally, wavelet-domain HMT-based image superresolutions are performed for the three channels of the low-resolution color image using the same posterior state probabilities, which reflect the hidden states of the wavelet coefficients of the high-resolution gray-scale image obtained before, and thus the resulting high-resolution color image is what we desired. Because the correlations among the three channels of a RGB color image are considered, there are no color distortions in the reconstructed high-resolution image. Experimental results show that the reconstructed color images have high PSNR and are of high visual quality.

关键词： wavelets Hidden Markov Tree color image superresolution data fusion image transform

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An algorithm for identification based on spectral diagnostic feature matching technology

An algorithm for identification based on spectral diagnostic...

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3rd international symposium on multispectral image processing and pattern recognition

作者： Chen, ZC Geng, XR Zhang, B Tong, QX Zheng, LF Chinese Acad Sci Inst Remote Sensing Applicat Beijing 100101 Peoples R China

ISBN: (纸本)0819451819

Imaging spectrometers acquire images in a large number, narrow, contiguous spectral bands to enable the extraction of reflectance spectra at a pixel scale that can be used for identification. Many identification methods based on the spectra match technique have been developed. Such as spectral angle mapping, binary encoding. But these methods use all the data in the spectral dimension and compare the whole similarity between the reference and test spectrum. Sometimes two different kinds of spectrums may have big similarity, and this results in the wrong identification. There are also many algorithms using, waveform characters for identification. However these methods maybe ineffective when the spectra have no diagnostic absorption feature. This paper introduces a new algorithm for identification based on diagnostic feature matching technique. Spectral matching technique and waveform characterization are combined for identification. In stead of matching, test spectrum in all the wavelength range, this new algorithm emphasizes diagnostic features' location and only matches several diagnostic features in their most possible locations. To insure the identification accuracy, spectral characters in terms of slope and asymmetry are used to check and verify. The algorithm is processed in four steps which will be described in the second part of this paper. In the third part, this algorithm is tested by identifying Alunite from AVIRIS image in Cuprite, Colorado. The result proved this new algorithm effective.

关键词： hyperspectral image identification spectra matching diagnostic features characterization

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Hierarchical indexing scheme for fast search in large-scale image database

Hierarchical indexing scheme for fast search in large-scale ...

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3rd international symposium on multispectral image processing and pattern recognition

作者： Ye, HJ Xu, GY Tsinghua Univ Dept Comp Sci & Technol Beijing 100084 Peoples R China

ISBN: (纸本)0819451819

Practical content-based image retrieval systems require efficient indexing schemes for fast k-nearest neighbor (k-NN) searches. Researchers have proposed many tree-based methods using space and data partitioning for similarity searches. However, traditional indexing methods perform poorly and will degrade to simple sequential scans at high dimensionality - that is so-called "curse of dimensionality". Recently, several filtering approaches based on vector approximation (VA) were proposed and showed promising performance. However, VA-based approaches need compute the bound of the distance between each feature vector and the query. It will consume the same computational overhead as the brute-force sequential scan. In this paper, a novel hierarchical indexing scheme is proposed. This approach integrates VA-based index structure with approximate NN (ANN) searches and performs probabilistic ANN searches on approximate vectors. Experiments show the proposed approach achieves a remarkable reduction of computational overhead and disk accesses for k-NN searches. This presented approach supports quadratic-form distance metric and can integrate with relevance feedback techniques for practical large-scale image retrieval systems.

关键词： content-based image retrieval (CBIR) k-nearest neighbor (k-NN) search high-dimensional indexing curse of dimensionality vector approximation (VA) approximate NN (ANN) search

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The use of wavelet high frequency substitution fusion to increase remote sensing image spatial resolution

The use of wavelet high frequency substitution fusion to inc...

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3rd international symposium on multispectral image processing and pattern recognition

作者： Bagan, H Ma, JW Li, QQ Liu, ZL Han, XZ Acad Sinica Inst Remote Sensing Applicat Beijing 100101 Peoples R China

ISBN: (纸本)0819451819

IHS transform was one of typical method for remote sensing data fusion. In resent years, newly developed method that combines advantages of IHS and Wavelet algorithms makes image fusion. In this case after the Wavelet substitution based on pixels or features, and then transforms inversely with IHS in Munsell color space. In this paper we introduce a high frequency substitution method to improve spatial resolutions of imagery. The procedure of the method introduced as flowchart, in which the dot line area is our newly added method. The resolution was greatly improved comparing original image. In cooperating with the demand of on going Minjiang river, Si Chuan, China. A 15m resolution PAN band and 30m resolution 7 bands of ETM data were selected for the method testing. the steps of method test showing in flow chart of this paper. in the figure the dots area was our newly developed wavelet high frequency substitute. Improved NDVI imagery raised the quality for monitoring land cover change factor in the project of Return Farmland Back to Forest or Grassland.

关键词： data fusion wavelet transform IHS transform part substitution

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Detecting small moving target in image sequences using optical flow based on the Discontinuous Frame Difference

Detecting small moving target in image sequences using optic...

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3rd international symposium on multispectral image processing and pattern recognition

作者： Qu, YS Tian, WJ Li, YC Chinese Acad Sci Xian Inst Opt & Precis Mech Xian 710068 Peoples R China

ISBN: (纸本)0819451819

A new concept that is the Discontinuous Frame Difference in image sequences is proposed in this paper, and is applied to the Optical Flow Algorithm. The modified Optical Flow Algorithm overcomes the shortcoming of the traditional Optical Flow Algorithm that cannot detect the small moving target whose moving displacement is less than one pixel between two continuous frames. The infinite norm of the Discontinuous Frame Difference Vector is used to preprocess the image sequences to get rid of most of pixels that are not the pixels of the moving target. And then the instantaneous velocities of the pixels remained by preprocessing is calculated by the Optical Flow Algorithm. If the pixels in an area have the moving continuity and consistency, a moving object is determined. For the preprocessing is able to get rid of most of pixels, the calculation quantity of the Optical Flow is reduced greatly. But the preprocessing is probable to lose some candidate pixels of the moving target, so the Gray Intensity Analysis is used to find these pixels back again. The Discontinuous Frame Difference Optical Flow Field Algorithm can be composed of the parallel structure system, which can detect different kinds of moving objects with different velocities. The experiment result proves the effectiveness of the method.

关键词： detection optical flow small moving target Discontinuous Frame Difference gray intensity analysis

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A new technique creates realistic 3D free-form surfaces from photographs and paintings

A new technique creates realistic 3D free-form surfaces from...

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third international symposium on multispectral image processing and pattern recognition

作者： Zhao, Ting Tan, Zheng Dep. of Electronic Engineering Xi'an Jiaotong University Xi'an 710049 China

This paper presents a novel approach for creating 3D free-form scene models from a single painting or photograph with no prior knowledge about the shape. The new technique takes as input a sparse set of user -specified constraints, and generates a well-behaved 3D surface satisfying the parameters. As each constraint is specified, the system recalculates and displays the reconstruction in real time. In contrast to previous work in single view reconstruction, our technique enables high quality reconstructions of free -form curved surfaces. A key feature of the approach is a novel hierarchical transformation technique for accelerating convergence on a non-uniform, piecewise continuous grid. The technique is interactive and updates the model in real time as constraints are added, allowing fast reconstruction of photorealistic scene models. The approach is shown to yield high quality results.

关键词： image reconstruction

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DTM modelling from remote sensing image with high resolution

DTM modelling from remote sensing image with high resolution

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3rd international symposium on multispectral image processing and pattern recognition

作者： Zhang, JQ Zheng, SY Zhang, Y Wuhan Univ Coll Remote Sensing & Informat Engn Wuhan 430079 Peoples R China

ISBN: (纸本)0819451819

Two key important techniques of DTM (Digital Terrain Model) modelling from remote sensing image pair with high resolution (RSIPHR) are introduced in this paper. First is determining conjugate point pair automatically by image matching. Because the approximate epipolar image pair of some RSIPHR remains quite large y-parallax after relative registration, 2 D relaxation matching should be used in the DTM Modeling. The procedure includes feature point extraction, approximate value estimation, matching and refining, check and filter. Second is the calculation of the spatial coordinates for the conjugate point pair from matching. For IKONOS and QUICKBIRD images, it can be based on the RPC/RPB (Rational Polynomial Coefficients) parameters. Because the coordinate accuracy, computed by RPC/RPB parameters, is quite lower in many cases, a block adjustment with an affine transform of image coordinates should be completed based on some control points. Then, the affine transform and RPC/RPB parameters should be used in computation. If there are control points more than 4, the new, simple and strict geometric model based on affine transformation could be applied for any RSIPHR. Only 8 affine coefficients and one slantwise angle need to be determined by control points. Then, they can be applied in the computation.

关键词： DTM (digital terrain model) remote sensing image high resolution 2D relaxation affine model

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Creating model of planar object from a single perspective line drawing

Creating model of planar object from a single perspective li...

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3rd international symposium on multispectral image processing and pattern recognition

作者： Chu, J Gao, MT Chen, GD Northwestern Polytech Univ Xian 710072 Peoples R China

ISBN: (纸本)0819451819

In this paper, a new approach for automatic creating boundary-representation of planar objects from single line drawings depicting the objects was proposed. Compared with other reconstruction methods that use information about points, for computer vision and CAD system, it was more convenient to use information about lines to interpret line drawings of polyhedral objects. The coordinates relations between a three-dimensional line and its perspective projection in two-dimension plane are given. Another three coordinates relations;are also provided, namely, the coordinates relations when two lines are intersecting, parallel, cross, perpendicular, relations when a line is parallel with or belongs to a plane, and relations when two planes parallel each other. On the basis of these coordinates relations, some new constraints about lines and planes are proposed and a linear system is established, The 3D information of planar objects can be obtained by solving the linear system. In the interpretation of line drawing of a planar object, it is proved that there are at least four degrees of freedom. Some examples are given.

关键词： reconstruction line drawing perspective projection planar objects computer vision interpretation image understanding CAD model coordinate

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Face detection based on a new nonlinear color space

Face detection based on a new nonlinear color space

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3rd international symposium on multispectral image processing and pattern recognition

作者： Liu, ZF You, ZS Wang, YQ Sichuan Univ Dept Comp Sci Inst Image & Graph Chengdu 610064 Peoples R China

ISBN: (纸本)0819451819

Color is a useful piece of information in computer vision especially for skin detection. In this paper, we propose a novel approach for skin segmentation and facial feature extraction. The proposed skin segmentation is a method for integrating the chrominance components of nonlinear YCrCb color model. The chrominance components of nonlinear YCrCb color space were modeled using a subgaussian probability density function, and then the face skin was segmented based on this function. In order to authenticate the face candidate regions, firstly, texture information in face candidate regions would be segmented using mean and variance of luminance information, and then eye would be located by the PCA edge direction information, and finally, the others features, such as nose and mouth, also were detected using the geometrical shape information. As all the above-mentioned techniques are simple and efficient, the proposed skin segmentation based on nonlinear color space method is invariability of lighting and pose. In our experiments, the proposed method has been successfully evaluated using two different test datasets. The detection accuracy is around 98%, the average run time ranged from 0.1-0.3 sec per frame.

关键词： face detection color transform PCAED eye detection facial feature

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Feature selection using a radial basis function neural network based on fuzzy set theoretic measure

Feature selection using a radial basis function neural netwo...

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3rd international symposium on multispectral image processing and pattern recognition

作者： Jia, P Sang, N Huazhong Univ Sci & Technol Inst Pattern Recognit & Artificial Intelligence Wuhan 430074 Hubei Peoples R China

ISBN: (纸本)0819451819

A new method for feature selection using radial basis function neural networks based on fuzzy set theoretic measure is proposed. The network's input values are all the membership of f feature values in a certain sample appertaining to C classes ( f : the amount of features, C : the amount of classes). Here, the fuzzy set theoretic pi measure based on the normal distribution is used for computing the membership. Hence, there are f * C pi measurements that are used as the inputs of the neural network. A radial basis function neural network with increasing hidden nodes is trained for classification, which is believed to be able to perfectly simulate the nonlinear relevance among the inputs. And then, we set zero to the C input nodes concerning one feature (we call this input vector the revised input vector), which means, as far as this feature is concerned, it belongs to none of the classes, which is considered to be the real delinking. The deviation between the output corresponding to the revised input vector and the expected output corresponding to the unrevised one is thought to denote the impact and the importance of this feature. Through this way, we may rank the features and select a suitable feature subset. Effectiveness of this algorithm is demonstrated on several sets of data, and compared with the effect of the feature-based node-prune MLP neural network method.

关键词： feature selection almost-1-to-1mapping RRF neural network

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