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BMBC: Bilateral Motion Estimation with Bilateral Cost Volume for Video Interpolation
European Conference on Computer Vision, 2020Video interpolation increases the temporal resolution of a video sequence by synthesizing intermediate frames between two consecutive frames. We propose a novel deep-learning-based video interpolation algorithm based on bilateral motion estimation. First,
Jun-ho Park +3 more
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3D Human Motion Estimation via Motion Compression and Refinement
Asian Conference on Computer Vision, 2020We develop a technique for generating smooth and accurate 3D human pose and motion estimates from RGB video sequences. Our method, which we call Motion Estimation via Variational Autoencoder (MEVA), decomposes a temporal sequence of human motion into a ...
Zhengyi Luo +2 more
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IEEE Transactions on Industrial Informatics, 2020
Three-dimensional display and virtual reality technology have been applied in minimally invasive surgery to provide doctors with a more immersive surgical experience. One of the most popular systems based on this technology is the Da Vinci surgical robot
Ling Li +5 more
semanticscholar +1 more source
Three-dimensional display and virtual reality technology have been applied in minimally invasive surgery to provide doctors with a more immersive surgical experience. One of the most popular systems based on this technology is the Da Vinci surgical robot
Ling Li +5 more
semanticscholar +1 more source
Motion Pyramid Networks for Accurate and Efficient Cardiac Motion Estimation
International Conference on Medical Image Computing and Computer-Assisted Intervention, 2020Cardiac motion estimation plays a key role in MRI cardiac feature tracking and function assessment such as myocardium strain. In this paper, we propose Motion Pyramid Networks, a novel deep learning-based approach for accurate and efficient cardiac ...
Hanchao Yu +5 more
semanticscholar +1 more source
Motion Estimation via Belief Propagation
14th International Conference on Image Analysis and Processing (ICIAP 2007), 2007We present a probabilistic model for motion estimation in which motion characteristics are inferred on the basis of a finite mixture of motion models. The model is graphically represented in the form of a pairwise Markov Random Field network upon which a Loopy Belief Propagation algorithm is exploited to perform inference.
Giuseppe Boccignone +3 more
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Advanced Motion Estimation and Motion Compensated De-Interlacing
International Workshop on HDTV '96, 1996This paper describes a new high quality de-interlacing algorithm applying motion estimation and compensation techniques. First, a comparison between two recently introduced de-interlacing concepts will be presented. One method is based on a generalized sampling theorem and the other uses time-recursion.
Bellers, E. B., de Haan, G.
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Memory-centric motion estimator
18th International Conference on VLSI Design held jointly with 4th International Conference on Embedded Systems Design, 2005In the streaming video processing domain, the only way to meet strict performance and quality requirements and yet to provide the area- and power-wise optimal platform is to apply buffering of the pixel data. Hence, the importance of careful design of the memory subsystem of streaming video SoC is significant. This paper presents the design of a memory
Beric, A. +3 more
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Motion estimation optimization
[Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing, 1992Motion estimation is cast as a problem in energy minimization. This is achieved by modeling the displacement field as a Markov random field. The equivalence of a Markov random field and a Gibbs distribution is then used to convert the problem into one of defining an appropriate energy function that describes the motion and any constraints imposed on it.
S.A. Rajala +3 more
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2010 IEEE International Conference on Image Processing, 2010
Motion estimation is known to be a non-convex optimization problem. This non-convexity comes from several ambiguities in motion estimation such as the aperture problem, or fast motion relative to the magnitude of the image gradient. In this paper, we propose a fast random search algorithm to estimate motion.
Sylvain Boltz, Frank Nielsen
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Motion estimation is known to be a non-convex optimization problem. This non-convexity comes from several ambiguities in motion estimation such as the aperture problem, or fast motion relative to the magnitude of the image gradient. In this paper, we propose a fast random search algorithm to estimate motion.
Sylvain Boltz, Frank Nielsen
openaire +1 more source