Titlebook: Video Data Compression for Multimedia Computing; Statistically Based Hua Harry Li,Shan Sun,Haluk Derin Book 1997 Springer Science+Business

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Spatial Temporal Prediction for Video Data Compression,rrelation of video images. The mathematical formulation extends the current existing spatial correlation model to both spatial and temporal domain. Our proposed technique can be described mathematically as the minimization of an objective function under the least mean square error criterion. With th

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On Gibbs-Markov Models for Motion Computation, are in video compression at various bit rates, video processing and image analysis. First, Gibbs-Markov models that define the relationship between images and motion descriptors is discussed. These models are usually incorporated into the so-called “likelihood” term in the Bayesian estimation. Then

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,Visual Motion Processing and Detection — from Biology to Modeling,such as the primary visual cortex (V1), the middle temporal (MT) area, and the medial superior temporal (MST) area. Specifically, the following topics are discussed: (a) the selectivity for local motion direction of the V1 neurons and the component MT neurons, (b) the aperture problem and the select

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Object Tracking using Hierarchical 2-D Mesh Modeling for Content Based Video Compression,osed method can be used in a video compression algorithm supporting content-based scalability and manipulation, where image regions can be processed independently. Our method tracks the evolution of a region of interest, identified at a reference frame, by estimating its motion and intensity variati

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Separation of Moving Regions from Background in an Image Sequence Acquired with a Mobil Camera,ing from the camera movement. Alternatively, the same scheme can be used to track a particular region of interest of the scene. The apparent motion induced by the camera motion is represented by a 2D parametric motion model, and compensated for using the values of the motion model parameters estimat

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