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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.
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Seeing Through the Clouds With DeepWaterMap
TL;DR: The next-generation surface water mapping model, DeepWaterMapV2, is presented, which uses improved model architecture, data set, and a training setup to create surface water maps at lower cost, with higher precision and recall, and is memory efficient for large inputs.
Proceedings ArticleDOI
Making image quality assessment robust
TL;DR: This work develops a robust framework for natural scene statistic model based blind image quality assessment (IQA) and shows how robustifying the model makes IQA approach resilient against deviation in model assumptions, small variations in the distortions and amount of data the model is trained on.
Journal ArticleDOI
Snakules: A Model-Based Active Contour Algorithm for the Annotation of Spicules on Mammography
Gautam S. Muralidhar,Alan C. Bovik,J David Giese,Mehul Sampat,Gary J. Whitman,Tamara Miner Haygood,Tanya W. Stephens,Mia K. Markey +7 more
TL;DR: A novel, model-based active contour algorithm, termed “snakules”, for the annotation of spicules on mammography, which deploys snakules that are converging open-ended active contours also known as snakes at each suspect spiculated mass location.
Proceedings ArticleDOI
Image features that draw fixations
TL;DR: A data-driven approach that uses eye tracking in tandem with principal component analysis to extract low-level image features that attract human gaze that resemble derivatives of the 2D Gaussian operator is described.
Journal ArticleDOI
Study of 3D Virtual Reality Picture Quality
TL;DR: In the study, 450 distorted images obtained from 15 pristine 3D VR images modified by 6 types of distortion of varying severities were evaluated by 42 subjects in a controlled VR setting and made available as part of the new database, in hopes that the relationships between gaze direction and perceived quality might be better understood.