Visual pattern analysis is a fundamental tool in mining data for knowledge. Computational representations for patterns and texture allow us to summarize, store, compare, and label in order to learn about the physical world. Our ability to capture visual imagery with cameras and sensors has resulted in vast amounts of raw data, but using this information effectively in a task-specific manner requires sophisticated computational representations. We enumerate specific desirable traits for these representations: (1) intraclass invariance--to support recognition; (2) illumination and geometric invariance for robustness to imaging conditions; (3) support for prediction and synthesis to use the model to infer continuation of the pattern; (4) support for change detection to detect anomalies and perturbations; and (5) support for physics-based interpretation to infer system properties from appearance. In recent years, computer vision has undergone a metamorphosis with classic algorithms adapting to new trends in deep learning. This text provides a tour of algorithm evolution including pattern recognition, segmentation and synthesis. We consider the general relevance and prominence of visual pattern analysis and applications that rely on computational models.
About the Author: Dr. Kristin J. Dana received a Ph.D. from Columbia University (New York, NY) in 1999, an M.S. degree from Massachusetts Institute of Technology in 1992 (Cambridge, MA), and a B.S. degree in 1990 from the Cooper Union (New York, NY). She is currently a Full Professor in the Department of Electrical and Computer Engineering at Rutgers University. She is also a member of the graduate faculty of Rutgers Computer Science Department. Prior to academia, Dr. Dana was on the research staff at Sarnoff Corporation a subsidiary of SRI (formerly Stanford Research Institute), developing real-time motion estimation algorithms for applications in defense, biomedicine, and entertainment industries. She is the recipient of the General Electric "Faculty of the Future" fellowship in 1990, the Sarnoff Corporation Technical Achievement Award in 1994 for the development of a practical algorithm for the real-time alignment of visible and infrared video images, the 2001 National Science Foundation Career Award for a program investigating surface science for vision and graphics, and a team recipient of the Charles Pankow Innovation Award in 2014 from the ASCE. Dr. Dana's research expertise is in computer vision including computational photography, machine learning, quantitative dermatology, illumination modeling, texture and reflectance models, optical devices, and applications of robotics. On these topics, she has published over 70 papers in leading journals and conferences.