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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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Journal ArticleDOI
Detecting and Mapping Video Impairments
Todd Goodall,Alan C. Bovik +1 more
TL;DR: This work presents a novel approach to conducting no-reference artifact detection in digital videos, implemented as an efficient and unique dual-path (parallel) excitatory/inhibitory neural network that uses a simple discrimination rule to define a bank of accurate distortion detectors.
Proceedings ArticleDOI
Audio-visual speech recognition for a vowel discrimination task
Peter L. Silsbee,Alan C. Bovik +1 more
TL;DR: In this paper, a speaker dependent lipreading system is developed, which uses hidden Markov modeling, a well known and highly successful technique for audio-based ASR, which is used to improve the robustness and accuracy of AutomaticSpeech Recognition (ASR).
Journal ArticleDOI
Computer-aided detection of breast cancer - have all bases been covered?
Gautam S. Muralidhar,Tamara Miner Haygood,Tanya W. Stephens,Gary J. Whitman,Alan C. Bovik,Mia K. Markey +5 more
TL;DR: It is argued that computer-aided detection will become an increasingly important tool for radiologists in the early detection of breast cancer, but there are some important issues that need to be given greater focus in designing CAD systems if they are to reach their full potential.
Proceedings ArticleDOI
Calibrating MS-SSIM for compression distortions using MLDS
TL;DR: It is shown how the data collected by MLDS allows us to recalibrate MS-SSIM to improve its performance and to quantify supra-threshold perceptual differences between pairs of images and to examine how perceived image quality changes as the compression rate is increased.
Proceedings ArticleDOI
Vergence control using a hierarchical image structure
Changhoon Yim,Alan C. Bovik +1 more
TL;DR: This paper presents a fast and reliable vergence control method using a hierarchical image structure for active vision systems that works well for a very large range of disparities and is not sensitive to calibration problems common to stereo images.