Optical Flow Estimation: An Error Analysis of Gradient-Based Methods with Local Optimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dynamic aspects in active vision
CVGIP: Image Understanding - Special issue on purposive, qualitative, active vision
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
Measuring visual motion from image sequences
Measuring visual motion from image sequences
Computer Vision and Image Understanding
Accurate optical flow computation under non-uniform brightness variations
Computer Vision and Image Understanding
Hardware implementation of optical flow constraint equation using FPGAs
Computer Vision and Image Understanding
Motion Analysis with the Radon Transform on Log-Polar Images
Journal of Mathematical Imaging and Vision
Computer Vision and Image Understanding
Accurate optical flow computation under non-uniform brightness variations
Computer Vision and Image Understanding
Hardware implementation of optical flow constraint equation using FPGAs
Computer Vision and Image Understanding
A review of log-polar imaging for visual perception in robotics
Robotics and Autonomous Systems
Space variant representations for mobile platform vision applications
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part II
A quantitative comparison of speed and reliability for log-polar mapping techniques
ICVS'11 Proceedings of the 8th international conference on Computer vision systems
A Novel Space Variant Image Representation
Journal of Mathematical Imaging and Vision
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Foveating vision sensors are important in both machine and biological vision. The term space-variant or foveating vision refers to sensor architectures based on smooth variation of resolution across the visual field, like that of the human visual system. Traditional image processing techniques do not hold when applied directly to such a image representation since the translation symmetry and the neighborhood structure in the spatial domain is broken by the space-variant properties of the sensor. Unfortunately, there has been little systematic development of image processing tools that are explicitly designed for foveated vision. 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 陋variable window潞 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, provide a consistent interpretation, and be 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.