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Its'hak Dinstein
Researcher at Ben-Gurion University of the Negev
Publications - 105
Citations - 22493
Its'hak Dinstein is an academic researcher from Ben-Gurion University of the Negev. The author has contributed to research in topics: Image processing & Image segmentation. The author has an hindex of 27, co-authored 105 publications receiving 20455 citations. Previous affiliations of Its'hak Dinstein include IBM & University of Kansas.
Papers
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Textural Features for Image Classification
TL;DR: These results indicate that the easily computable textural features based on gray-tone spatial dependancies probably have a general applicability for a wide variety of image-classification applications.
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New maximum likelihood motion estimation schemes for noisy ultrasound images
Boaz Cohen,Its'hak Dinstein +1 more
TL;DR: Two new maximum likelihood motion estimation schemes for ultrasound images are presented, based on the assumption that both images are contaminated by a Rayleigh distributed multiplicative noise, which enables motion estimation in cases where a noiseless reference image is not available.
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Geometric separation of partially overlapping nonrigid objects applied to automatic chromosome classification
Gady Agam,Its'hak Dinstein +1 more
TL;DR: The proposed approach treats the separation problem as an identification problem, and, in this way, manages to segregate overlapping chromosomes in a metaphase image, and is fast and does not depend on the existence of a separating path.
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A comparative study of neural network based feature extraction paradigms
TL;DR: Tested on five real-world databases, the MLP provides the highest classification accuracy at the cost of deforming the data structure, whereas the linear models preserve the structure but usually with inferior accuracy.
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Intuition, perception, and secure communication
TL;DR: The possibility of integrating human visual intelligence into the process of encrypting sensitive information by presenting certain visual information to the recipient's eye is discussed, which adds a new dimension to the cryptocomplexity of such a process.