Thrasyvoulos Pappas plenary: Visual Signal Analysis: Focus on Texture Similarity

Texture is an important visual attribute both for human perception and image analysis systems. In this plenary talk, Thrasyvoulos Pappas of Northwestern University, presents new structural texture similarity metrics and applications that critically depend on such metrics, with emphasis on image compression and content-based retrieval.

The new metrics account for human visual perception and the stochastic nature of textures. They rely entirely on local image statistics and allow substantial point-by-point deviations between textures that according to human judgment are similar or essentially identical.

Pappas presents new testing procedures for objective texture similarity metrics and identifies three operating domains for evaluating the performance of such similarity metrics.

Thrasyvoulos Pappas received his PhD degree in electrical engineering and computer science from MIT in 1987. His research interests are in human perception and electronic media, and in particular, image and video quality and compression, image and video analysis, content-based retrieval, model-based halftoning, and tactile and multimodal interfaces. Professor Pappas is a Fellow of SPIE and the IEEE.