Robot vision
Digital Image Registration Using Projections
IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of image registration techniques
ACM Computing Surveys (CSUR)
Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
The robust estimation of multiple motions: parametric and piecewise-smooth flow fields
Computer Vision and Image Understanding
Accuracy vs efficiency trade-offs in optical flow algorithms
Computer Vision and Image Understanding
Scientific Computing: An Introductory Survey
Scientific Computing: An Introductory Survey
Hierarchical Model-Based Motion Estimation
ECCV '92 Proceedings of the Second European Conference on Computer Vision
A model of the effect of image motion in the Radon transform domain
IEEE Transactions on Image Processing
Projection-based image registration in the presence of fixed-pattern noise
IEEE Transactions on Image Processing
Journal of Mathematical Imaging and Vision
Motion Analysis with the Radon Transform on Log-Polar Images
Journal of Mathematical Imaging and Vision
PCS'09 Proceedings of the 27th conference on Picture Coding Symposium
Human face processing with 1.5D models
AMFG'07 Proceedings of the 3rd international conference on Analysis and modeling of faces and gestures
Edge projection-based image registration
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
Nonrigid motion estimation from a sequence of degraded images
Mathematical and Computer Modelling: An International Journal
Multidimensional Systems and Signal Processing
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The demand for more effective compression, storage, and transmission of video data is ever increasing. To make the most effective use of bandwidth and memory, motion-compensated methods rely heavily on fast and accurate motion estimation from image sequences to compress not the full complement of frames, but rather a sequence of reference frames, along with “differences” between these frames which results from estimated frame-to-frame motion. Motivated by the need for fast and accurate motion estimation for compression, storage, and transmission of video as well as other applications of motion estimation, we present algorithms for estimating affine motion from video image sequences. Our methods utilize properties of the Radon transform to estimate image motion in a multiscale framework to achieve very accurate results. We develop statistical and computational models that motivate the use of such methods, and demonstrate that it is possible to improve the computational burden of motion estimation by more than an order of magnitude, while maintaining the degree of accuracy afforded by the more direct, and less efficient, 2-D methods.