Optical Flow with an Intensity-Weighted Smoothing

Authors:
J. Aisbett
Affiliations:
Surveillance Research Laboratory, Salisbury, SA
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
1989

Citing 5
Cited 8

An Investigation of Smoothness Constraints for the Estimation of Displacement Vector Fields from Image Sequences

IEEE Transactions on Pattern Analysis and Machine Intelligence
Incremental reconstruction of 3D scenes from multiple, complex images

Artificial Intelligence
The image flow constraint equation

Computer Vision, Graphics, and Image Processing
Optical Flow Estimation: An Error Analysis of Gradient-Based Methods with Local Optimization

IEEE Transactions on Pattern Analysis and Machine Intelligence
Measurement of Visual Motion

Measurement of Visual Motion

An Iterated Estimation of the Motion Parameters of a Rigid Body from Noisy Displacement Vectors

IEEE Transactions on Pattern Analysis and Machine Intelligence
The computation of optical flow

ACM Computing Surveys (CSUR)
The Frequency Structure of One-Dimensional Occluding Image Signals

IEEE Transactions on Pattern Analysis and Machine Intelligence
On Functionals with Greyvalue-Controlled Smoothness Terms for Determining Optical Flow

IEEE Transactions on Pattern Analysis and Machine Intelligence
A Correlation-Relaxation-Labeling Framework for Computing Optical Flow - Template Matching from a New Perspective

IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive Grid Refinement Procedures for Efficient Optical Flow Computation

International Journal of Computer Vision
Incremental estimation of image flow using a Kalman filter

Journal of Visual Communication and Image Representation
Nonrigid motion estimation from a sequence of degraded images

Mathematical and Computer Modelling: An International Journal

Quantified Score

Hi-index	0.16

Visualization

Abstract

Matching algorithms which use image structure such as edges and junctions cannot perform well on unstructured images. For such images, global gradient-based methods may be more appropriate. Some of the assumptions involved in applying discretized conservation equations such as the optical flow equation are discussed and an intensity-weighted method of estimating dense displacement fields is presented, which attempts to avoid some of the problems of the standard methods. The only derivative required is the intensity gradient, and the method includes procedures for automatic parameter evaluation. The algorithm is expected to perform better than conventional algorithms on images without strong texture, as is demonstrated on the test set of images.