The time complexity of the adaptive mean shift is related to the dimension of data and the number of iterations. The computational complexity will increase proportionally with the increase of the data dimension. An ap...
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The time complexity of the adaptive mean shift is related to the dimension of data and the number of iterations. The computational complexity will increase proportionally with the increase of the data dimension. An approximate neighborhood queries method is presented for the computation of high dimensional data, in which, the localitysensitive hashing(LSH) is used to reduce the computational complexity of the adaptive mean shift algorithm. Experimental results show that the proposed algorithm can reduce the complexity of the adaptive mean shift algorithm and can produce a more accurate classification than the fixed bandwidth mean shift algorithm.
In this paper, we propose a method for robot self-position identification by active sound localization. This method can be used for autonomous security robots working in room environments. A system using an AIBO robot...
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Accurate detection of moving object provides a fundamental capability that drives numerous high-level computer vision applications. In this paper, a novel algorithm is proposed to detect objects in widely varying ther...
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The time complexity of the adaptive mean shift is related to the dimension of data and the number of iterations. The computational complexity will increase proportionally with the increase of the data dimension. An ap...
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The time complexity of the adaptive mean shift is related to the dimension of data and the number of iterations. The computational complexity will increase proportionally with the increase of the data dimension. An approximate neighborhood queries method is presented for the computation of high dimensional data, in which, the locality-sensitive hashing (LSH) is used to reduce the computational complexity of the adaptive mean shift algorithm. Experimental results show that the proposed algorithm can reduce the complexity of the adaptive mean shift algorithm and can produce a more accurate classification than the fixed bandwidth mean shift algorithm.
In order to get the change detection *** unsupervised change detection algorithm for multi-temporal satellite image based on NSCT (non-subsampling contourlet transform) and k-means clustering is proposed in this paper...
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In order to get the change detection *** unsupervised change detection algorithm for multi-temporal satellite image based on NSCT (non-subsampling contourlet transform) and k-means clustering is proposed in this paper. For each pixel in the log-ratio image, multi-scale and multi-direction feature vector is extracted by NSCT and the reconstruction of the log-ratio image is obtained. The threshold is produced by using the k-means clustering algorithm and can distinguish between the unchanged and the change region. Finally, the change detection map is achieved. Some satellite images are used to verify the proposed method and the results shows that it has a higher stability and accuracy against Gaussian and speckle noise than traditional algorithms.
In this paper, a technique is presented for the fusion of Panchromatic (PAN) and low spatial resolution multispectral (MS) images to get high spatial resolution of the latter. In this technique, we apply PCA transform...
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In this paper, a technique is presented for the fusion of Panchromatic (PAN) and low spatial resolution multispectral (MS) images to get high spatial resolution of the latter. In this technique, we apply PCA transformation to the MS image to obtain the principal component (PC) images. A NSCT transformation to PAN and each PC images for N level of decomposition. We use FOCC as criterion to select PC. And then, we use the relative entropy as criterion to reconstruct high-frequency detailed images. Finally, we apply inverse NSCT to selected PC's low-frequency approximate image and reconstructed high- frequency detailed images to obtain high spatial resolution MS image. The experimental results obtained by applying the proposed image fusion method indicate some improvements in the fusion performance.
A new method for image denoising based on the free distributed hypothesis test threshold (FDR) and the non-sub-sampled contourlet transform(NSCT) is proposed in this paper. This method firstly acquires the free distri...
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A new method for image denoising based on the free distributed hypothesis test threshold (FDR) and the non-sub-sampled contourlet transform(NSCT) is proposed in this paper. This method firstly acquires the free distributed false discovery rate hypotheses test in statistics to set the threshold in the NSCT domain, and then removes the noise through soft threshold function, which doesn’t depend on the length of signal. The experimental results show that the proposed method can more effectively reduce Gaussian noise and improve the peak value signal-to-noise ratio in the remote sensing image; Meanwhile, this method utilizes the shift invariant of NSCT transform to inhibit the pseudo Gibbs distortion effect, and integrally preserves the texture and edge etc.. details’ information of the image, thus obviously ameliorate the visual effect of the image.
By considering the strong correlation between wavelet coefficients of the actual image, while bivariate model is only a statistical model for the interscale dependency of wavelet coefficient with parent coefficient, w...
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By considering the strong correlation between wavelet coefficients of the actual image, while bivariate model is only a statistical model for the interscale dependency of wavelet coefficient with parent coefficient, without taking into account the correlation of adjacent coefficient. Therefore, based on the shift-invariance and better directionality of the dual-tree complex wavelet transfer (DTCWT) and incorporating neighboring wavelet coefficients with BiShrink, a novel BiShrink threshold and DTCWT remote sensing image denoising method is presented. Experimental results show the proposed algorithm gets better PSNR than other methods mentioned observably. In terms of visual quality the proposed algorithm can get the images with more details smooth profiles and aliasing is restricted
Aiming at the cu rrent structured P2P system's locality of physical location and accessing resources, in the context of P4P technology, this paper takes the Pastry algorithm as a foundation, proposes a P4P routing...
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Analysis on the basis of the protocol Gnutella0.6, the use P4P technologies for sensing conveniently network topology information, proposes a P4P-based Gnutella routing algorithm, in which nodes join algorithm to cons...
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