Mode of access: World Wide Web.

System requirements: Adobe Acrobat Reader.

Part of: Synthesis digital library of engineering and computer science.

Series from website.

Includes bibliographical references (p. 71-78).

1. Introduction -- 1.1 Registration and motion estimation -- 1.2 Block-based motion estimation -- 1.3 Optical flow -- 1.4 Conventions -- 1.5 Organization of the book --

2. Control grid interpolation (CGI) -- 2.1 Conventional CGI formulation -- 2.1.1 One-dimensional -- 2.1.2 Two-dimensional -- 2.2 Multiresolution and adaptive CGI formulations -- 2.3 Optimization mathematics -- 2.3.1 One-dimensional control grid and one degree of freedom optical flow -- 2.3.2 Two dimensional control grid and one degree of freedom optical flow -- 2.3.3 Two-dimensional control grid and two degrees of freedom optical flow -- 2.4 Symmetric implementations -- 2.5 Summary --

3. Application of CGI to registration problems -- 3.1 Registration of one-dimensional data: inter-vector registration -- 3.1.1 Dynamic timewarping -- 3.1.2 Isophote identification -- 3.2 Registration of two-dimensional data: inter-image registration -- 3.2.1 Motion estimation -- 3.2.2 Mitigation of atmospheric turbulence distortion -- 3.2.3 Medical image registration -- 3.3 Summary --

4. Application of CGI to interpolation problems -- 4.1 Interpolation of 1D data: inter-vector interpolation -- 4.1.1 Single-image super-resolution -- 4.1.2 Video deinterlacing -- 4.2 Interpolation of 2D data: inter-image interpolation -- 4.2.1 Inter-frame interpolation -- 4.2.2 Inter-slice interpolation -- 4.3 Summary --

5. Discussion and conclusions -- 5.1 Strengths and weaknesses -- 5.2 Application to higher-dimensions and multivariate optimization -- 5.3 Final thoughts and conclusions --

Bibliography -- Authors' biographies.

Abstract freely available; full-text restricted to subscribers or individual document purchasers.

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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.

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Title from PDF t.p. (viewed on January 18, 2013).