GIBBS RANDOM FIELD MODEL BASED 3-D MOTION ESTIMATION BY WEAKENED RIGIDITY

Date

1994-01-01

Author

Alatan, Abdullah Aydın

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3-D motion estimation from a video sequence remains a challenging problem. Modelling the local interactions between the 3-D motion parameters is possible by using Gibbs random fields. An energy function which gives the joint probability distribution of the motion vectors, is constructed. The most probable motion vector set is found by maximizing the probability, represented by this distribution. Since the 3-D motion estimation problem is ill-posed, the regularization is achieved by an initial rigidity assumption. Afterwards, the rigidity is weakened hierarchically, until the finest level is reached. At the finest level, each point has its own motion vector and the "weak-connection" between these vectors are described by the energy function. The high computational cost Q decreased considerably by the multiprecision approach. The simulation results support all our discussions.

Subject Keywords

Motion estimation, Cost function, Equations, Power engineering and energy, Video sequences, Probability distribution, Computational modeling, Layout, Image segmentation, Image restoration

URI

https://hdl.handle.net/11511/41003

DOI

https://doi.org/10.1109/icip.1994.413679

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

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Object Segmentation in Multi-view Video via Color, Depth and Motion Cues Cigla, Cevahir; Alatan, Abdullah Aydın (2009-01-01) In the light of dense depth map estimation, motion estimation and object segmentation, the research on multi-view video (MVV) content has becoming increasingly popular due to its wide application areas in the near future. In this work, object segmentation problem is studied by additional cues due to depth and motion fields. Segmentation is achieved by modeling images as graphical models and performing popular Normalized Cuts method with some modifications. In the graphical models, each node is represented b...
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3-D motion estimation of rigid objects for video coding applications using an improved iterative version of the E-matrix method Alatan, Abdullah Aydın (1998-02-01) As an alternative to current two-dimensional (2-D) motion models, a robust three-dimensional (3-D) motion estimation method is proposed to be utilized in object-based video coding applications, Since the popular E-matrix method is well known for its susceptibility to input errors, a performance indicator, which tests the validity of the estimated 3-D motion parameters both explicitly and implicitly, is defined. This indicator is utilized within the RANSAC method to obtain a robust set of 2-D motion correspo...
TEMPORALLY CONSISTENT LAYER DEPTH ORDERING VIA PIXEL VOTING FOR PSEUDO 3D REPRESENTATION Turetken, Engin; Alatan, Abdullah Aydın (2009-05-06) A new region-based depth ordering algorithm is proposed based on the segmented motion layers with affine motion models. Starting from an initial set of layers that are independently extracted for each frame of an input sequence, relative depth order of every layer is determined following a bottom-to-top approach from local pair-wise relations to a global ordering. Layer sets of consecutive time instants are warped in two opposite directions in time to capture pair-wise occlusion relations of neighboring lay...

Citation Formats

A. A. Alatan, “GIBBS RANDOM FIELD MODEL BASED 3-D MOTION ESTIMATION BY WEAKENED RIGIDITY,” 1994, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/41003.