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Bernice E. Rogowitz
Researcher at IBM
Publications - 101
Citations - 4085
Bernice E. Rogowitz is an academic researcher from IBM. The author has contributed to research in topics: Visualization & Image segmentation. The author has an hindex of 31, co-authored 100 publications receiving 3985 citations.
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Patent
System and method for measuring image similarity based on semantic meaning
TL;DR: In this paper, a method for deriving a plurality of semantic categories for representing important semantic cues in images, where each semantic category is modeled through a combination of perceptual features that define the semantics of that category and that discriminate that category from other categories, forming a set of the perceptual features comprising required features and frequently occurring features.
Journal ArticleDOI
Adaptive perceptual color-texture image segmentation
TL;DR: The proposed approach combines knowledge of human perception with an understanding of signal characteristics in order to segment natural scenes into perceptually/semantically uniform regions to convey semantic information that can be used for content-based retrieval.
Predicting Visible Differences in High Dynamic Range Images - Model and its Calibration
Rafal Mantiuk,Scott J. Daly,Karol Myszkowski,Hans-Peter Seidel,Bernice E. Rogowitz,Thrasyvoulos N. Pappas,Scott J. Daly +6 more
TL;DR: Several modifications to the Visual Difference Predicator (VDP) are proposed, which improve the prediction of perceivable differences in the full visible range of luminance and under the adaptation conditions corresponding to real scene observation.
Proceedings ArticleDOI
A rule-based tool for assisting colormap selection
TL;DR: PRAVDAColor, implemented as a module in the IBM Visualization Data Explorer, provides the user a selection of appropriate colormaps given the data type and spatial frequency, the user's task, and properties of the human perceptual system.
Journal ArticleDOI
How not to lie with visualization
TL;DR: Variations in the method of representing the data can significantly influence the user's perception and interpretation of the data.