| 000 | 05263nam a2200625 i 4500 | ||
|---|---|---|---|
| 001 | 6813380 | ||
| 003 | IEEE | ||
| 005 | 20200413152908.0 | ||
| 006 | m eo d | ||
| 007 | cr cn |||m|||a | ||
| 008 | 130118s2013 caua foab 000 0 eng d | ||
| 020 | _a9781627051309 (electronic bk.) | ||
| 020 | _z9781627051293 (pbk.) | ||
| 024 | 7 |
_a10.2200/S00461ED1V01Y201212ASE011 _2doi |
|
| 035 | _a(CaBNVSL)swl00402001 | ||
| 035 | _a(OCoLC)824620717 | ||
| 040 |
_aCaBNVSL _cCaBNVSL _dCaBNVSL |
||
| 050 | 4 |
_aTA1637 _b.Z823 2013 |
|
| 082 | 0 | 4 |
_a621.367 _223 |
| 100 | 1 | _aZwart, Christine M. | |
| 245 | 1 | 0 |
_aControl grid motion estimation for efficient application of optical flow _h[electronic resource] / _cChristine M. Zwart and David H. Frakes. |
| 260 |
_aSan Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA) : _bMorgan & Claypool, _cc2013. |
||
| 300 |
_a1 electronic text (viii, 79 p.) : _bill., digital file. |
||
| 490 | 1 |
_aSynthesis lectures on algorithms and software in engineering, _x1938-1735 ; _v# 11 |
|
| 538 | _aMode of access: World Wide Web. | ||
| 538 | _aSystem requirements: Adobe Acrobat Reader. | ||
| 500 | _aPart of: Synthesis digital library of engineering and computer science. | ||
| 500 | _aSeries from website. | ||
| 504 | _aIncludes bibliographical references (p. 71-78). | ||
| 505 | 0 | _a1. 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 -- | |
| 505 | 8 | _a2. 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 -- | |
| 505 | 8 | _a3. 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 -- | |
| 505 | 8 | _a4. 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 -- | |
| 505 | 8 | _a5. Discussion and conclusions -- 5.1 Strengths and weaknesses -- 5.2 Application to higher-dimensions and multivariate optimization -- 5.3 Final thoughts and conclusions -- | |
| 505 | 8 | _aBibliography -- Authors' biographies. | |
| 506 | 1 | _aAbstract freely available; full-text restricted to subscribers or individual document purchasers. | |
| 510 | 0 | _aCompendex | |
| 510 | 0 | _aINSPEC | |
| 510 | 0 | _aGoogle scholar | |
| 510 | 0 | _aGoogle book search | |
| 520 | 3 | _aMotion 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. | |
| 530 | _aAlso available in print. | ||
| 588 | _aTitle from PDF t.p. (viewed on January 18, 2013). | ||
| 650 | 0 |
_aImage processing _xDigital techniques. |
|
| 653 | _amotion estimation | ||
| 653 | _aimage registration | ||
| 653 | _ainterpolation | ||
| 653 | _aoptimization methods | ||
| 653 | _apiecewise linear techniques | ||
| 700 | 1 | _aFrakes, David H. | |
| 776 | 0 | 8 |
_iPrint version: _z9781607051293 |
| 830 | 0 | _aSynthesis digital library of engineering and computer science. | |
| 830 | 0 |
_aSynthesis lectures on algorithms and software in engineering ; _v# 11. _x1938-1735 |
|
| 856 | 4 | 2 |
_3Abstract with links to resource _uhttp://ieeexplore.ieee.org/servlet/opac?bknumber=6813380 |
| 999 |
_c561955 _d561955 |
||