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Ishwar K. Sethi
Researcher at University of Rochester
Publications - 154
Citations - 5178
Ishwar K. Sethi is an academic researcher from University of Rochester. The author has contributed to research in topics: Feature detection (computer vision) & Artificial neural network. The author has an hindex of 33, co-authored 153 publications receiving 5012 citations. Previous affiliations of Ishwar K. Sethi include Oakland University & Wayne State University.
Papers
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Journal ArticleDOI
Scanline algorithms in the JPEG discrete cosine transform compressed domain
Bo Shen,Ishwar K. Sethi +1 more
TL;DR: It is shown how the scanline algorithms for rotation and projective mapping can be developed for JPEG/DCT images and their performance is evaluated with respect to quality, speed, and control and memory overhead.
Proceedings ArticleDOI
Scanline algorithms in compressed domain
Bo Shen,Ishwar K. Sethi +1 more
TL;DR: This work shows how the scanline algorithms for rotation and projective mapping can be developed for JPEG/DCT images and their performance is evaluated with respect to quality, speed, and control and memory overhead.
Proceedings ArticleDOI
Extraction of diagnostic rules using neural networks
TL;DR: It is shown that the proposed approach can disregard irrelevant features in the data and can generate different criteria combinations indicating the presence of systemic lupus erythematosus in a patient.
Journal ArticleDOI
A Fine-Tuned Convolution Neural Network Based Approach For Phenotype Classification Of Zebrafish Embryo
TL;DR: A fine-tuned convolution neural network (CNN) based model is proposed for automated classification of different phenotypical changes observed due to the toxic substance in the zebrafish embryo to demonstrate the ability of CNN model as well as a fine- Tuned CNN based model to classify different deformation in an embryo with high accuracy.
Region Growing Using Online Learning.
TL;DR: This paper presents a region growing approach for color images using an online learning algorithm that aims for content-based image retrieval systems that follows a variation of Bayesian estimation procedure to characterize each region as it is grown.