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The application of image super-resolution technologies in recent years has increased noticeably. The main purpose of image up-scaling is to obtain high-resolution images from low-resolution images, and these up-scaled images should keep satisfactory visual qualities and present natural textures. The most popular image up-scaling algorithms are based on interpolation methods in spatial domain. However,...
This paper discusses how to assess video Quality of Experience (QoE) with image feature information which includes texture and saliency information. In order to compress and transmit the feature information, wavelet transform is conducted and the high-frequency component histograms are fitted using generalized Gaussian distribution. At end user side, the video distortion is measured by using Kullback-Leibler...
Wavelet analysis is a newly emerging signal processing method. Because of its time-frequency resolution property, it can be used to analyze the frequency spectrum of non-stationary signal in the field of structural health monitoring. This article constructs the feather vector by using wavelet packet energy. By using the feature element in feather vector, the Loose Factor (LF) is constructed to detect...
This paper presents a new face recognition method based on the analysis of local features. Firstly, we can get the images of magnitude by means of analyzing face images with the Gabor wavelets. Secondly, the magnitude images are divided into blocks, then principle components analysis (PCA) could be directly used to all the blocks to construct the feature space. Finally, all the blocks of images are...
HVS (Human vision system) has the advantages in multi-resolution, target tracking, rapid feature extraction. In this paper, we analyze vision projection model and contrast sensitivity function of the human vision first, then analyze and compare the biological characteristics of human vision and the characteristics of the wavelet algorithm. And we design the method to build the bionic vision system...
We introduce the use of over-complete spherical wavelets for shape analysis of 2D closed surfaces. Bi-orthogonal spherical wavelets have been shown to be powerful tools in the segmentation and shape analysis of 2D closed surfaces, but unfortunately they suffer from aliasing problems and are therefore not invariant under rotations of the underlying surface parameterization. In this paper, we demonstrate...
In vivo quantification of neuroanatomical shape variations is possible due to recent advances in medical imaging and has proven useful in the study of neuropathology and neurodevelopment. In this paper, we apply a spherical wavelet transformation to extract shape features of cortical surfaces reconstructed from magnetic resonance images (MRIs) of a set of subjects. The spherical wavelet transformation...
Advances in medical imaging technique make it possible to study shape variations of neuroanatomical structures in vivo, which has been proved useful in the study of neuropathology and neurodevelopment. In this paper, we propose the use of spherical wavelet transformation to extract shape features, as it can characterize the underlying functions in a local fashion in both space and frequency, in contrast...
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