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Motion estimation is a long-standing cornerstone of image and video processing. Most notably, motion estimation serves as the foundation for many of today's ubiquitous video coding standards including H.264. Motion estimators also play key roles in countless other applications that serve the consumer, industrial, biomedical, and military sectors. Of the many available motion estimation techniques, optical flow is widely regarded as most flexible. The flexibility offered by optical flow is particularly useful for complex registration and interpolation problems, but comes at a considerable computational expense. As the volume and dimensionality of data that motion estimators are applied to continue to grow, that expense becomes more and more costly. Control grid motion estimators based on optical flow can accomplish motion estimation with flexibility similar to pure optical flow, but at a fraction of the computational expense. Control grid methods also offer the added benefit of representing motion far more compactly than pure optical flow. This booklet explores control grid motion estimation and provides implementations of the approach that apply to data of multiple dimensionalities. Important current applications of control grid methods including registration and interpolation are also developed. Table of Contents: Introduction / Control Grid Interpolation (CGI) / Application of CGI to Registration Problems / Application of CGI to Interpolation Problems / Discussion and Conclusions
List of contents
Introduction.- Control Grid Interpolation (CGI).- Application of CGI to Registration Problems.- Application of CGI to Interpolation Problems.- Discussion and Conclusions.
About the author
Christine M. Zwart received B.S.E. and M.S. degrees in bioengineering from Arizona State University where she is currently pursuing a Ph.D. in the same. She is a graduate researcher and doctoral candidate in the Image Processing Applications Lab working on image enlargement and enhancement algorithms with medical, defense, and consumer applications under the advisement of David H. Frakes. She is a National Science Foundation Graduate Research Fellow, a Science Foundation Arizona Graduate Research Fellow, a 2012 Arizona State University Faculty Women's Association Outstanding Graduate Student, and was engineering intern at The Boeing Company. After graduating she plans to work in medical imaging informatics at the Mayo Clinic. David H. Frakes received B.S. and M.S. degrees in electrical engineering, an M.S. in mechanical engineering, and a Ph.D. degree in bioengineering, all from the Georgia Institute of Technology. In 2003, he co-founded 4-D Imaging, Inc., a small business that provides image and video processing solutions for the biomedical and military sectors. In 2008, he joined the faculty at Arizona State University (ASU) where he currently serves as a jointly appointed assistant professor in the School of Biological and Health Systems Engineering and the School of Electrical, Computer, and Energy Engineering. Professor Frakes was the ASU Centennial Professor of the Year in 2009, received the IEEE Outstanding University Faculty Award in 2011, and was awarded the National Science Foundation CAREER Award in 2012.He manages the Image Processing Applications Laboratory at ASU, which focuses on problems in image and video processing, machine vision, and fluid dynamics.
