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In this paper, a new corner detector is proposed based on evolution difference of scale pace, which can well reflect the change of the domination feature between the evolved curves. In Gaussian scale space we use Difference of Gaussian (DoG) to represent these scale evolution differences of planar curves and the response function of the corners is defined(More)
Compressed sensing MRI (CS-MRI) aims to significantly reduce the measurements required for image reconstruction in order to accelerate the overall imaging speed. The sparsity of the MR images in transformation bases is one of the fundamental criteria for CS-MRI performance. Sparser representations can require fewer samples necessary for a successful(More)
Magnetic Resonance Imaging (MRI) is an essential medical imaging tool limited by the data acquisition speed. Compressed Sensing is a newly proposed technique applied in MRI for fast imaging with the prior knowledge that the signals are sparse in a special mathematic basis (called the 'sparsity' basis). During the exploitation of the sparsity in MR images,(More)
Compressed sensing (CS) theory has been recently applied in Magnetic Resonance Imaging (MRI) to accelerate the overall imaging process. In the CS implementation, various algorithms have been used to solve the nonlinear equation system for better image quality and reconstruction speed. However, there are no explicit criteria for an optimal CS algorithm(More)
A new method based on the weighted fusion of multiple models is presented for wavelength selection in multivariate calibration of spectral data. It fuses the regression coefficients of multiple models with weights based on minimum mean square error to improve the accuracy and stability of the wavelength selection. To validate the performance of the proposed(More)
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