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The sparsity of the input signal is important for compressive sensing (CS) reconstruction in CS system. In this paper, we establish an optimized truncation model to determine the number of the sparsified coefficients to be truncated in CS acquisition according to the sampling rate. The proposed truncation model suits for signals of any dimension. With the truncation model, the sparsity of the signal...
This paper proposes a new probability iterative closest point approach with bounded scale based on expectation maximization (EM) estimation for scaling registration of point sets with noise. The bounded scale ICP algorithm can handle the case with different scales, but it could not effectively yield the alignment of point sets with noise. Aiming at improving the registration precision, a Gaussian...
The segmentation of categorized objects addresses the problem of joint segmentation of a single category of object across a collection of images, where categorized objects are referred to objects in the same category. Most existing methods of segmentation of categorized objects made the assumption that all images in the given image collection contain the target object. In other words, the given image...
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