Download Academic Press Library in Signal Processing, Volume 5: Image by Sergios Theodoridis, Rama Chellappa PDF

By Sergios Theodoridis, Rama Chellappa

This 5th quantity, edited and authored through global best specialists, provides a assessment of the rules, equipment and strategies of significant and rising examine themes and applied sciences in photo and video compression and multimedia.

With this reference resource you will:

  • Quickly seize a brand new zone of analysis
  • Understand the underlying ideas of a subject matter and its application
  • Ascertain how a subject pertains to different components and research of the learn concerns but to be resolved
    • Quick educational reports of vital and rising subject matters of study in photograph and Video Compression and Multimedia
    • Comprehensive references to magazine articles and different literature on which to construct extra, extra particular and certain knowledge
    • Edited through leading people in the sphere who, via their popularity, were in a position to fee specialists to jot down on a selected topic

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    Extra resources for Academic Press Library in Signal Processing, Volume 5: Image and Video Compression and Multimedia

    Example text

    Segmentation-based motion estimation and spatio-temporal segmentation are addressed in [11]. In [12], a Bayesian framework is presented that combines motion estimation and segmentation. In the context of video coding, such a representation requires to transmit the shape of the region, which entails a bit rate overhead. Block-based: As a special case of the region-based support, a very frequent choice is to simply partition the image into blocks. If the block size is sufficiently small, then the assumption that the block is moving in a coherent way is likely to be valid.

    Efficient and accurate motion estimation is an essential component in the domains of image sequence analysis, computer vision, and video communications. In the context of image sequence analysis and computer vision, the objective of motion estimation algorithms is to precisely and faithfully model the motion in the scene. This information is fundamental for video understanding and object tracking. Relevant applications include video surveillance, robotics, autonomous vehicle navigation, human motion analysis, quality control in manufacturing, video search and retrieval, and video restoration.

    It is called the motion constraint or the optical flow equation. We can make the two following observations: • • The determination of the real displacement in the sequence from the motion constraint is underdetermined, since we dispose of only one equation and we have two unknown variables (the optical flow components u and v). 2, can be easily understood from the vector form of the equation. In this case, we have an inner product between V and the gradient of the image. Clearly, only the component of the velocity vector parallel to the gradient can be determined.

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