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Yehoshua Y. Zeevi
Researcher at Technion – Israel Institute of Technology
Publications - 415
Citations - 8067
Yehoshua Y. Zeevi is an academic researcher from Technion – Israel Institute of Technology. The author has contributed to research in topics: Image processing & Wavelet. The author has an hindex of 47, co-authored 413 publications receiving 7777 citations. Previous affiliations of Yehoshua Y. Zeevi include Rutgers University & Columbia University.
Papers
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Journal ArticleDOI
The generalized Gabor scheme of image representation in biological and machine vision
Moshe Porat,Yehoshua Y. Zeevi +1 more
TL;DR: It is shown that there exists a tradeoff between the number of frequency components used per position and thenumber of such clusters (sampling rate) utilized along the spatial coordinate.
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Image enhancement and denoising by complex diffusion processes
TL;DR: It is proved that the imaginary part is a smoothed second derivative, scaled by time, when the complex diffusion coefficient approaches the real axis, and developed two examples of nonlinear complex processes, useful in image processing.
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The farthest point strategy for progressive image sampling
TL;DR: A new method of farthest point strategy for progressive image acquisition-an acquisition process that enables an approximation of the whole image at each sampling stage-is presented, retaining its uniformity with the increased density, providing efficient means for sparse image sampling and display.
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Forward-and-backward diffusion processes for adaptive image enhancement and denoising
TL;DR: The proposed structure tensor is neither positive definite nor negative, and switches between these states according to image features, resulting in a forward-and-backward diffusion flow where different regions of the image are either forward or backward diffused according to the local geometry within a neighborhood.
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Integrated active contours for texture segmentation
TL;DR: This work addresses the issue of textured image segmentation in the context of the Gabor feature space of images and shows that combining boundary and region information yields more robust and accurate texture segmentation results.