CIF: Small: Visual Recognition and Restoration In Concert

University Of California-Santa Cruz, Santa Cruz CA

Investigators

Abstract

Project Abstract for NSF Proposal 1016018 Visual Recognition and Restoration In Concert Peyman Milanfar Electrical Engineering Department University of California at Santa Cruz In this research effort a central challenge in computer vision is addressed: Namely, to recognize and enhance objects in complex visual scenes given imperfect images, and more generally, video data. This effort strengthens the theoretical and practical foundations for generic visual object recognition systems that can deal with significant variations in visual appearance, a large number of categories, and stochastically and systematically degraded data. Data imperfections can include random noise, blur, and environmental degradations. The approach has transformative potential for a broad range of practical applications such as scalable image search and retrieval, automatic annotation, surveillance and security, video forensics, and medical image analysis for computer-aided diagnosis. The research advances the state-of-the-art in two important ways: (a) a unified and robust framework is derived for both (2-D) object and (3-D) action recognition, even when the data is subject to significant distortions, and (b) recognition and restoration from degraded data are treated in a common, statistically optimal setting. Traditionally, recognition and restoration have been addressed with limited awareness of each other?s techniques and of potential commonalities in approach. By improving, generalizing, and refining previously separate approaches to recognition with degraded data in an adaptive, non-parametric setting, for both 2-D and 3-D, this project contributes to the technical foundations and toolkits that can connect computer vision and image processing intelligently.