Optical Flow Estimation: An Error Analysis of Gradient-Based Methods with Local Optimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Performance of optical flow techniques
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real-time tracking of moving objects with an active camera
Real-Time Imaging - Special issue on computer vision motion analysis
Rapid Anisotropic Diffusion Using Space-Variant Vision
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optical Flow Computation in the Log-Polar-Plane
CAIP '95 Proceedings of the 6th International Conference on Computer Analysis of Images and Patterns
Computation of 3-D-Motion Parameters Using the Log-Polar Transform
CAIP '95 Proceedings of the 6th International Conference on Computer Analysis of Images and Patterns
Measuring visual motion from image sequences
Measuring visual motion from image sequences
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In this article we propose a novel approach to compute the optical flow directly on log-mapped images. We propose the use of a generalized dynamic image model (GDIM) based method for computing the optical flow as opposed to the brightness constancy model (BCM) based method. We introduce a new notion of "variable window" and use the space-variant form of gradient operator while computing the spatio-temporal gradient in log-mapped images for a better accuracy and to ensure that the local neighborhood is preserved. We emphasize that the proposed method must be numerically accurate, provides a consistent interpretation and is capable of computing the peripheral motion. Experimental results on both the synthetic and real images have been presented to show the efficacy of the proposed method.