Robert M. Gray Stanford University
The research is about the theory and design of systems for communicating and interpreting information bearing signals such as images, video, and speech. The main focus is on methods for reducing the complexity of signals while retaining or extracting essential information. The algorithms typically quantize or compress the data efficiently into a simpler signal that can be transmitted or stored efficiently and then used in place of the original. They can facilitate identification of the data as belonging to some class or type. Examples of such signal processing include analog-to-digital conversion, speech and image coding, speech recognition, and segmenting images into distinct regions of interest. The research involves the fundamental theory of such systems and draws on ideas from information theory, statistical signal processing, and statistics. It also involves applications, especially algorithms for classifying and segmenting images and for content-addressable browsing through image databases. The basic tools come from modeling, density estimation, compression, coding, classification, and segmentation. They enable both theoretical characterizations of optimal performance and associated algorithms by which codes are optimized for specific applications, for example, systems for compression and segmentation of images for communications, analysis, and retrieval. Tools are drawn from four fundamental ideas of information theory and signal processing: vector quantization, mixture models, relative entropy (Kullback-Leibler information) measures of distortion or distance between probability distributions, and universal coding. Of particular interest is the theory of high rate vector quantization and its applications to statistical image classification and segmentation. Specific applications of interest include automatic segmentation of multimodal images and of categorizing images taken internally in gas pipelines in order to quantify pipeline integrity.
