Journal ArticleDOI
An efficient two-pass MAP-MRF algorithm for motion estimation based on mean field theory
Jie Wei,Ze-Nian Li +1 more
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TLDR
The proposed two-pass algorithm is much faster than any other MAP-MRF motion estimation method reported in the literature so far and is supported by the experimental results from both synthetic and real-world image sequences.Abstract:
This paper presents a two-pass algorithm for estimating motion vectors from image sequences. In the proposed algorithm, the motion estimation is formulated as a problem of obtaining the maximum a posteriori in the Markov random field (MAP-MRF). An optimization method based on the mean field theory (MFT) is opted to conduct the MAP search. The estimation of motion vectors is modeled by only two MRFs, namely, the motion vector field and unpredictable field. Instead of utilizing the line field, a truncation function is introduced to handle the discontinuity between the motion vectors on neighboring sites. In this algorithm, a "double threshold" preprocessing pass is first employed to partition the sites into three regions, whereby the ensuing MPT-based pass for each MRF is conducted on one or two of the three regions. With this algorithm, no significant difference exists between the block-based and pixel-based MAP searches any more. Consequently, a good compromise between precision and efficiency can be struck with ease. To render our algorithm more resilient against noise, the mean absolute difference instead of mean square error is selected as the measure of difference, which is more reliable according to the knowledge of robust statistics. This is supported by our experimental results from both synthetic and real-world image sequences. The proposed two-pass algorithm is much faster than any other MAP-MRF motion estimation method reported in the literature so far.read more
Citations
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Robot vision
TL;DR: A scheme is developed for classifying the types of motion perceived by a humanlike robot and equations, theorems, concepts, clues, etc., relating the objects, their positions, and their motion to their images on the focal plane are presented.
Journal ArticleDOI
Multiple Exposure Fusion for High Dynamic Range Image Acquisition
Takao Jinno,Masahiro Okuda +1 more
TL;DR: This paper presents an efficient and accurate multiple exposure fusion technique for the HDRI acquisition, which simultaneously estimates displacements and occlusion and saturation regions by using maximum a posteriori estimation and constructs motion-blur-free HDRIs.
Proceedings ArticleDOI
Motion blur free HDR image acquisition using multiple exposures
Takao Jinno,Masahiro Okuda +1 more
TL;DR: The high dynamic range image (HDRI) acquisition method based on Markov random field model is proposed, which estimates displacements, occlusion and saturated regions, and by using them construct the motion blur free HDRI with higher quality than other existing methods.
Journal ArticleDOI
MAP-Based Motion Refinement Algorithm for Block-Based Motion-Compensated Frame Interpolation
TL;DR: Experimental results prove that the proposed motion vector field refinement algorithm achieves performances comparable with those of several existing MAP-based BME algorithms at a much lower computational complexity.
References
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