Yunlong Sheng

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We propose radial harmonic Fourier moments, which are shifting, scaling, rotation, and intensity invariant. Compared with Chebyshev-Fourier moments, the new moments have superior performance near the origin and better ability to describe small images in terms of image-reconstruction errors and noise sensitivity. A multidistortion-invariant(More)
Image descriptors based on the circular-Fourier-radial-Mellin transform are used for position-, rotation-, scale-, and intensity-invariant multiclass pattern recognition. The orders of the radial moments and of the circular harmonics are chosen to obtain an efficient image description. The first-order radial moments of three circular harmonics are(More)
A shift-invariant optical continuous wavelet transform is used for pattern recognition. We propose an Voptical wavelet matched filter that performs optical wavelet transforms for edge enhancement and the correlation between two wavelet transforms in a single step. This new bandpass matched filter shows improved discrimination capability with respect to the(More)
We present a new method to register high and low resolution color images of the retina as well as high resolution angiographies. The registration method is based on global point mapping with blood vessel bifurcations as control points. We also present results of various image fusion algorithms to determine the most appropriate one. Registration and fusion(More)
In this paper, we apply a recently developed type of moments , Orthogonal Fourier-Mellin Moments (OFMMs) [7], to the specijic problem of fully translation-, scale-and in-plane rotation-invariant detection of human faces in two-dimensional static color images, and we compare theirper-formance with that of the generalized Hu's moments or non-orthogonal(More)