Motion Analysis and Image Sequence ProcessingM. Ibrahim Sezan, Reginald L. Lagendijk An image or video sequence is a series of two-dimensional (2-D) images sequen tially ordered in time. Image sequences can be acquired, for instance, by video, motion picture, X-ray, or acoustic cameras, or they can be synthetically gen erated by sequentially ordering 2-D still images as in computer graphics and animation. The use of image sequences in areas such as entertainment, visual communications, multimedia, education, medicine, surveillance, remote control, and scientific research is constantly growing as the use of television and video systems are becoming more and more common. The boosted interest in digital video for both consumer and professional products, along with the availability of fast processors and memory at reasonable costs, has been a major driving force behind this growth. Before we elaborate on the two major terms that appear in the title of this book, namely motion analysis and image sequence processing, we like to place them in their proper contexts within the range of possible operations that involve image sequences. In this book, we choose to classify these operations into three major categories, namely (i) image sequence processing, (ii) image sequence analysis, and (iii) visualization. The interrelationship among these three categories is pictorially described in Figure 1 below in the form of an "image sequence triangle". |
Contents
Chapter | 1 |
An Estimation Theoretic Perspective on Image Processing | 23 |
Chapter 3 | 53 |
Copyright | |
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Motion Analysis and Image Sequence Processing M. Ibrahim Sezan,Reginald L. Lagendijk Limited preview - 2012 |
Motion Analysis and Image Sequence Processing M. Ibrahim Sezan,Reginald L. Lagendijk No preview available - 2012 |
Common terms and phrases
accuracy adaptive addition algorithm analysis applications approach areas assume block camera changes chapter coder coding Commun complexity components compression computational considered constraint corresponding defined depends depth described detail detection determined direction discussed displacement edge effect efficient encoding equation error example facial Figure filter frame frequency function given global motion IEEE image sequence important improved input intensity interpolation linear matching matrix median method motion compensated motion estimation motion field motion vectors moving noise object observation obtained operations optical flow original parameters performance pixel plane possible prediction predictor presented problem processing quantization reconstructed reduce reference regions represents resolution respectively rotation sampling scene scheme segmentation shape shown shows signal smoothness spatial step structure subband subsampling techniques temporal trajectory transform translation values vector visual weighted zoom