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Motion estimation in hazy videos
Pattern Recognition Letters, 2021Abstract Motion estimation is the basic need for the success of many video analysis algorithms such as moving object detection, human activity recognition, etc. Most of the motion estimation algorithms are prone to weather conditions and thus, they fail to estimate the motion in degraded weather. Severe weather situations like snow, rain, haze, smog,
Sachin Chaudhary +4 more
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Predictive motion estimation with global motion predictor
SPIE Proceedings, 2004Predictive motion estimation techniques have become widely used in video coding systems due to their reasonable coding performance and low computational requirement compared to the brute force full search method. However, most existing predictive motion estimation algorithms focus mainly on local motions and consider motion predictions from only ...
Shijun Sun, Shawmin Lei
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Estimate Large Motions Using the Reliability-Based Motion Estimation Algorithm
International Journal of Computer Vision, 2006Detecting and estimating motions of fast moving objects has many important applications. However, most existing motion estimation techniques have difficulties in handling large motions in the scene. In this paper, we extend our recently proposed reliability-based stereo vision technique to solving large motion estimation problem.
Minglun Gong, Yee-Hong Yang
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Global motion estimation combining with motion segmentation
SPIE Proceedings, 2013In order to improve the accuracy of global motion estimation (GME), a new method for GME combining with motion segmentation is proposed in this paper. The proposed method removes motion vector (MV) outliers and implements initial motion segmentation by analyzing properties of motion vectors.
Yaxiang Zhao +2 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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Motion sickness estimation system
The 2012 International Joint Conference on Neural Networks (IJCNN), 2012Motion sickness occurs when the brain receives conflicting sensory information from body, inner ear and eyes [1]. In some cases, a decreased ability to actively control the body's postural motion also causes motion sickness [2][3]. Many previous studies have indicated that motion sickness had negative effect on driving performance, and sometimes lead ...
Chin-Teng Lin +5 more
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Estimating motion in image sequences
IEEE Signal Processing Magazine, 1999We have reviewed the estimation of 2D motion from time-varying images, paying particular attention to the underlying models, estimation criteria, and optimization strategies. Several parametric and nonparametric models for the representation of motion vector fields and motion trajectory fields have been discussed.
Christoph Stiller, Janusz Konrad
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Testing a motion estimator array
[1990] Proceedings of the International Conference on Application Specific Array Processors, 2002The authors examine the problems of testing a motion estimator array using a novel strategy for testing VLSI regular arrays. They present a case study to demonstrate the ability of the strategy to test regular arrays with data restrictions, to test large word level arrays, and to test less regular arrays.
William P. Marnane, Will R. Moore
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Object-guided motion estimation
Computer Vision and Image Understanding, 2016We provide a new not yet investigated approach for motion analysis in image sequences.We show the benefits of equipping a dense motion estimator with object-level tracking.We provide a new algorithm for object-aware and occlusion-aware point tracking.We evaluate quantitatively our method on available ground-truth trajectories.
Juan-Manuel Pérez-Rúa +2 more
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Image motion estimation by clustering
International Journal of Imaging Systems and Technology, 1990AbstractImage motion is estimated by matching feature “interest” points in different frames of video image sequences. The matching is based on local similarity of the displacement vectors. Clustering in the displacement vector space is used to determine the set of plausible match vectors.
Amit Bandopadhay, John Aloimonos
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