A precise tracking algorithm for small target based on event supervision is introduced in this paper. the target chains and object aggregation are established firstly, Tri-level scan filter contains grey intension fil...
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ISBN:
(纸本)9780819469502
A precise tracking algorithm for small target based on event supervision is introduced in this paper. the target chains and object aggregation are established firstly, Tri-level scan filter contains grey intension filter, shape filter and location filter, is adopted to implement data relevancy between target chains and object aggregation from coarse to fine. Movement trend of object to tracking target which is classified into follow, approach and leaving, and events include envelop and combination are detected, supervised and processed. On the other hand, based on the analysis of the error model for target centroid estimation, a recursive approach method for target centroid calculation with high precision is adopted in the algorithm. It combines tracking, recognition and prediction effectively base on the tracking theory of human eyes. Experimental results show that the method is feasible and effective.
According to the system intrinsic quality of self-comparability and the empirical mode decomposition algorithm of completeness and stability, an improvement algorithm for EMD image decomposition is presented. It is in...
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ISBN:
(纸本)9780819469502
According to the system intrinsic quality of self-comparability and the empirical mode decomposition algorithm of completeness and stability, an improvement algorithm for EMD image decomposition is presented. It is integrity, fast and effective. Some aspects were improved that bidimensional interpolation methods and end conditions of getting intrinsic mode image. three questions of general algorithm for EMD image decomposition were solved. First, the algorithm for image decomposition was slow;Second, some points were not contained because of delaunay triangulation;third, the end condition of algorithm was not accuracy. Experiments were made by Matlab and the validity of the improvement algorithm was validated.
Sequence image mosaic is an important and effective method to build a large "panoramic" scene which includes two main steps: image registration and intensity blending. In this paper, SIFT feature points are ...
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ISBN:
(纸本)9780819469502
Sequence image mosaic is an important and effective method to build a large "panoramic" scene which includes two main steps: image registration and intensity blending. In this paper, SIFT feature points are used to match images. SIFT are invariant to rotation, translation and scale changes, but the significant drawback is the high dimensional feature descriptor which lead to the expensive computation. So reduced SIFT descriptors are proposed to increase the speed of image registration. Linear combination methods of the matching points' gray-values are used for intensity blending. the experiments show that our method is useful and has high registration accuracy.
this paper presents a new method for multi-focus image fusion. In the method, the source images are first decomposed into blocks, and the decomposed images are then combined by the use of adaptive Wiener filter. Effec...
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ISBN:
(纸本)9780819469502
this paper presents a new method for multi-focus image fusion. In the method, the source images are first decomposed into blocks, and the decomposed images are then combined by the use of adaptive Wiener filter. Effects of the block size and threshold are analyzed, and comparison with wavelet transform based method is done. Experimental results show that the proposed method is comparative to wavelet transform based methods for the images without noise, while this method is computationally simpler, and can be implemented in real-time applications. Experimental results also show that under noise circumstances, additive noise or multiplicative noise, the proposed method is obviously superior to the wavelet based method.
In this paper, a new method named BQCGW (block-based method of combining quantized colors and Gabor wavelet features) is proposed for image retrieval. HSV color space, in which measured color differences are proportio...
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ISBN:
(纸本)9780819469502
In this paper, a new method named BQCGW (block-based method of combining quantized colors and Gabor wavelet features) is proposed for image retrieval. HSV color space, in which measured color differences are proportional to the human perception of such differences, is quantized into 67 kinds of representative colors. We also propose the use of Gabor wavelet features for texture analysis. images in the database are divided into nine blocks before extracting color and texture features. Experiment results show that our method is feasible and valid.
Rather than attempting to separate signal from noise in the spatial domain, it is often advantageous to work in a transform domain. Building on previous work, a novel denoising method based on local adaptive multi-sca...
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ISBN:
(纸本)9780819469502
Rather than attempting to separate signal from noise in the spatial domain, it is often advantageous to work in a transform domain. Building on previous work, a novel denoising method based on local adaptive multi-scale wavelet least squares support vector regression is proposed. Investigation on real images contaminated by Gaussian noise has demonstrated that the proposed method can achieve an acceptable trade off between the noise removal and smoothing of the edges and details.
this paper developed a new model of region extraction based on salient region detection and scale-space primal sketch. In the proposed model, we extract the region of interest (ROI) in two steps. Firstly, we estimate ...
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ISBN:
(纸本)9780819469519
this paper developed a new model of region extraction based on salient region detection and scale-space primal sketch. In the proposed model, we extract the region of interest (ROI) in two steps. Firstly, we estimate the extent of object by means of region detection, which considers the feature that contributes most to the saliency map. Secondly, we use the scale-space primal sketch to acquire an explicit representation of the significant image structure which gives a qualitative description of the scales and regions of interest. Finally, we combine the results from the two steps. Applications to extract ROI showed that this new model could lead to better results which can be used for guiding later stage processing.
Since 1986 Bayesian Network has been a hot study topic in artificial intelligence field. We have researched Bayesian Network and related algorithms in remote sensing data processing for five years. Recently we finishe...
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ISBN:
(纸本)9780819469519
Since 1986 Bayesian Network has been a hot study topic in artificial intelligence field. We have researched Bayesian Network and related algorithms in remote sensing data processing for five years. Recently we finished BayesNetEX which is an extensive edition software of Bayesian network algorithms for remote sensing imageprocessing and knowledge inference. the copyright is from Copyright Protection Center of China. this paper briefly introduces the main modules of BayesNetEX and demonstrates its classification application with an ETM+ image, which also shows some potential applications in remote sensing imageprocessing.
Aiming at the registration of optical remote sensing images, an algorithm based on strong edge region is proposed. First, the strong edge regions are extracted. then combines withthe regions' moment invariants an...
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ISBN:
(纸本)9780819469502
Aiming at the registration of optical remote sensing images, an algorithm based on strong edge region is proposed. First, the strong edge regions are extracted. then combines withthe regions' moment invariants and RANSAC method, it can obtain an accurate match of the strong edge regions. Utilizing the centroids of the matching regions as control points in the affine geometric distortion, an automatic registration is performed. A large number of experiments are fulfilled with SPOT and Quickbird satellite images and good results are obtained.
the so-called robust L1 PCA was introduced in our recent work [1] based on the L1 noise assumption. Due to the heavy tail characteristics of the L1 distribution, the proposed model has been proved much more robust aga...
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ISBN:
(纸本)9780819469502
the so-called robust L1 PCA was introduced in our recent work [1] based on the L1 noise assumption. Due to the heavy tail characteristics of the L1 distribution, the proposed model has been proved much more robust against data outliers. In this paper, we further demonstrate how the learned robust L1 PCA model can be used to denoise image data.
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