Results 321 to 330 of about 4,469,643 (384)

Motion Guided 3D Pose Estimation from Videos

European Conference on Computer Vision, 2020
We propose a new loss function, called motion loss, for the problem of monocular 3D Human pose estimation from 2D pose. In computing motion loss, a simple yet effective representation for keypoint motion, called pairwise motion encoding, is introduced ...
Jingbo Wang, Sijie Yan, Yuanjun Xiong, Dahua Lin   +3 more
semanticscholar   +1 more source

Optimal motion estimation

[1989] Proceedings. Workshop on Visual Motion, 2003
The problem of using feature correspondences to recover the structure and 3D motion of a moving object from its successive images is analyzed. They formulate the problem as a quadratic minimization problem with a nonlinear constraint. Then they derive the condition for the solution to be optimal under the assumption of Gaussian noise in the input, in ...
M.E. Spetsakis, J. Aloimonos
openaire   +1 more source

Motion estimation with integrated motion models

Proceedings of the 42nd annual Southeast regional conference, 2004
Conventional motion estimation algorithms rely on motion vectors characterizing translations and thus have limitations in capturing transformations of objects in video scenes such as scaling, rotations and deformations. In this paper, we introduce integrated motion models based on the Lie derivatives to improve the motion estimation accuracy.
Mahesh Nalasani, W. David Pan, Seong-Moo Yoo   +2 more
openaire   +1 more source

Motion estimation.

No abstract.
Haan, de, G., Ciuhu, C.
  +3 more sources

An Efficient VLSI Architecture for Fast Motion Estimation Exploiting Zero Motion Prejudgment Technique and a New Quadrant-Based Search Algorithm in HEVC

Circuits, systems, and signal processing, 2021
F. H. Shajin, P. Rajesh, M. Raja
semanticscholar   +1 more source

Fractional Motion Estimation

2009
Motion estimation in H.264/AVC supports quarter-pixel precision and is usually carried out in two phases: integer motion estimation (IME) and fractional motion estimation (FME). We have talked about IME in Chap.3. After IME finds an integer motion vector (IMV) for each of the 41 subblocks, FME performs motion search around the refinement center pointed
Youn-Long Steve Lin, Chao-Yang Kao, Huang-Chih Kuo, Jian-Wen Chen   +3 more
openaire   +1 more source

Motion estimation on interlaced video

SPIE Proceedings, 2005
Motion compensated de-interlacing and motion estimation based on Yen's generalisation of the sampling theorem (GST) have been proposed by Delogne and Vandendorpe. Motion estimation methods using three-fields have been designed on a block-by-block basis, minimising the difference between two GST predictions.
Ciuhu, Calina, de Haan, Gerard
openaire   +1 more source

Motion Estimation using Tangent Distance

2007 IEEE International Conference on Image Processing, 2007
In this paper, we present a method based on tangent distance to estimate motion in image sequences. Tangent distance combines an intuitive understanding and effective modeling of differences between patterns. This tool was first introduced and successfully applied in character recognition.
Fabrizio, Jonathan, Dubuisson, Séverine
openaire   +2 more sources

Simultaneous motion estimation and segmentation

IEEE Transactions on Image Processing, 1997
We present a Bayesian framework that combines motion (optical flow) estimation and segmentation based on a representation of the motion field as the sum of a parametric field and a residual field. The parameters describing the parametric component are found by a least squares procedure given the best estimates of the motion and segmentation fields. The
M M, Chang, A M, Tekalp, M I, Sezan
openaire   +2 more sources

Adaptive model-based motion estimation

IEEE Transactions on Image Processing, 1994
A general discrete-time, adaptive, multidimensional framework is introduced for estimating the motion of one or several object features from their successive nonlinear projections on an image plane. The motion model consists of a set of linear difference equations with parameters estimated recursively from a nonlinear observation equation.
R J, Crinon, W J, Kolodziej
openaire   +2 more sources