Results 221 to 230 of about 55,702 (256)

Optical flow and scene flow estimation: A survey

Pattern Recognition, 2021
Abstract Motion analysis is one of the most fundamental and challenging problems in the field of computer vision, which can be widely applied in many areas, such as autonomous driving, action recognition, scene understanding, and robotics. In general, the displacement field between subsequent frames can be divided into two types: optical flow and ...
Xuezhi Xiang, Mingliang Zhai, Ning Lv
exaly   +2 more sources

Occlusion-Aware Optical Flow Estimation

IEEE Transactions on Image Processing, 2008
Optical flow can be reliably estimated between areas visible in two images, but not in occlusion areas. If optical flow is needed in the whole image domain, one approach is to use additional views of the same scene. If such views are unavailable, an often-used alternative is to extrapolate optical flow in occlusion areas.
Janusz Konrad
exaly   +3 more sources

Constrained Optical Flow Estimation as a Matching Problem

IEEE Transactions on Image Processing, 2013
In general, discretization in the motion vector domain yields an intractable number of labels. In this paper, we propose an approach that can reduce general optical flow to the constrained matching problem by pre-estimating a 2-D disparity labeling map of the desired discrete motion vector function.
Mikhail Mozerov
exaly   +3 more sources

Estimating fluid optical flow

Proceedings 15th International Conference on Pattern Recognition. ICPR-2000, 2002
We address the problem of fluid motion estimation in image sequences. For such motions, standard optical flow methods, based on intensity conservation and spatial coherence of motion field, are not suitable. This is due to the highly deformable nature of a fluid medium.
Thomas Corpetti, Étienne Mémin, Patrick Pérez   +2 more
openaire   +1 more source

Robust optical flow estimation

Proceedings of 1st International Conference on Image Processing, 2002
The paper presents a robust algorithm for computation of optical flow using the principle of conservation of a set of semi-invariant local features that are representatives of local gray-level properties in an image. Specifically, a set of rotation-invariant local orthogonal Zernike moments is used as features.
Sugata Ghosal, Rajiv Mehrotra
openaire   +1 more source

Wavelet-based optical flow estimation

IEEE Transactions on Circuits and Systems for Video Technology, 2002
In this paper, a new algorithm for accurate optical flow estimation using discrete wavelet approximation is proposed. The proposed method takes advantages of the nature of wavelet theory, which can efficiently and accurately represent "things", to model optical flow vectors and image related functions.
Li-Fen Chen, Hong-Yuan Mark Liao, Ja-Chen Lin   +2 more
openaire   +1 more source

Optical flow estimation in aerated flows

Journal of Hydraulic Research, 2016
ABSTRACTOptical flow estimation is known from Computer Vision where it is used to determine obstacle movements through a sequence of images following an assumption of brightness conservation. This paper presents the first study on application of the optical flow method to aerated stepped spillway flows.
Bung, Daniel Bernhard (PD Prof. Dr.-Ing. habil.), Valero, Daniel (Dr.-Ing.)   +1 more
openaire   +3 more sources

Region-based optical flow estimation

Proceedings CVPR '89: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003
A correspondence method is developed for determining optical flow where the primitive motion tokens to be matched between consecutive time frames are regions. The computation of optical flow consists of three stages: region extraction, region matching, and optical flow smoothing. The computation is completed by smoothing the initial optical flow, where
Chiou-Shann Fuh, Petros Maragos
openaire   +1 more source

Simultaneous multiple optical flow estimation

[1990] Proceedings. 10th International Conference on Pattern Recognition, 2002
The authors propose a simultaneous closed-form estimation method for multiple optical flow from image sequences in which each image point has multiple motions. This method only requires convolution for space-time filtering and low-dimensional eigensystem analysis as an optimization process.
Masahiko Shizawa, Kenji Mase
openaire   +1 more source