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Alan C. Bovik
Researcher at University of Texas at Austin
Publications - 872
Citations - 120104
Alan C. Bovik is an academic researcher from University of Texas at Austin. The author has contributed to research in topics: Image quality & Video quality. The author has an hindex of 102, co-authored 837 publications receiving 96088 citations. Previous affiliations of Alan C. Bovik include University of Illinois at Urbana–Champaign & University of Sydney.
Papers
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Proceedings ArticleDOI
Perceptual tools for quality-aware video networks
TL;DR: This talk will review key perceptual principles that are, or could be used to create effective video quality prediction models, and leadingquality prediction models that utilize these principles.
Journal ArticleDOI
Noise unveils spatial frequency and orientation selectivity during visual search
TL;DR: The authors' data are consistent with earlier parafoveal studies, but provided additional insight into observers’ dynamic decision-making, highlighting different search strategies that predominate at different target frequencies and orientations.
Proceedings ArticleDOI
A new multimodal interactive way of subjective scoring of 3D video quality of experience
TL;DR: A new methodology that is multimodal, using aural and tactile cues to help engage and focus the subject(s) on their tasks, and makes it possible for multiple subjects to assess 3D QoE simultaneously in a large space such as a movie theater, and at di®erent visual angles and distances.
Dissertation
Discovery and representation of human strategies for visual search
TL;DR: It is shown that under uncertainty, observers rely on known target characteristics to direct their saccades and to select target candidates upon foveal scrutiny, and multiple orientation characteristics of targets are represented in observer search strategies, modulated by their sensitivity/selectivity for each orientation.
Journal ArticleDOI
Feature classification techniques in model-based object recognition
Scott T. Acton,Alan C. Bovik +1 more
TL;DR: A system that performs model‐based recognition of the projections of generalized cylinders, and a new feed‐forward “neural” implementation that utilizes the back‐propagation learning algorithm that yields a 31.8% reduction in classification error.