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Eli Saber
Researcher at Rochester Institute of Technology
Publications - 148
Citations - 5258
Eli Saber is an academic researcher from Rochester Institute of Technology. The author has contributed to research in topics: Image segmentation & Pixel. The author has an hindex of 28, co-authored 147 publications receiving 4782 citations. Previous affiliations of Eli Saber include Hewlett-Packard & University of Rochester.
Papers
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Proceedings ArticleDOI
Reversible data hiding
TL;DR: A prediction-based conditional entropy coder which utilizes static portions of the host as side-information improves the compression efficiency, and thus the lossless data embedding capacity.
Journal ArticleDOI
Lossless generalized-LSB data embedding
TL;DR: In this paper, a generalization of the well-known least significant bit (LSB) modification is proposed as the data-embedding method, which introduces additional operating points on the capacity-distortion curve.
Journal ArticleDOI
Hierarchical watermarking for secure image authentication with localization
TL;DR: In this paper, the authors propose a hierarchical watermarking scheme that divides the image into blocks in a multilevel hierarchy and calculates block signatures in this hierarchy. But the method is vulnerable to VQ counterfeiting attacks.
Journal ArticleDOI
Frontal-view face detection and facial feature extraction using color, shape and symmetry based cost functions
Eli Saber,A. Murat Tekalp +1 more
TL;DR: An algorithm for detecting human faces and facial features, such as the location of the eyes, nose and mouth, is described, using a supervised pixel-based color classifier and an ellipse model fit to each disjoint skin region.
Journal ArticleDOI
Automatic Image Segmentation by Dynamic Region Growth and Multiresolution Merging
L. Garcia Ugarriza,Eli Saber,Sreenath Rao Vantaram,Vincent J. Amuso,Mark Q. Shaw,Ranjit Bhaskar +5 more
TL;DR: A new unsupervised color image segmentation algorithm is proposed, which exploits the information obtained from detecting edges in color images in the CIE L*a*b* color space to identify some initial portion of the input image content.