Comutations underlying the measuremnt of visual motion.
Artificial Intelligence
Closed form solutions to image flow equations for planar surfaces in motion
Computer Vision, Graphics, and Image Processing
Proc. of the ACM SIGGRAPH/SIGART interdisciplinary workshop on Motion: representation and perception
On Smoothness of a Vector Field-Application to Optical Flow
IEEE Transactions on Pattern Analysis and Machine Intelligence
The analytic structure of image flows: deformation and segmentation
Computer Vision, Graphics, and Image Processing
Visual perception of three-dimensional motion
Neural Computation
Estimating 3D Egomotion from Perspective Image Sequence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Polynomial Methods for Structure from Motion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Subspace methods for recovering rigid motion I: algorithm and implementation
International Journal of Computer Vision
Robot Vision
On the motion of 3D curves and its relationship to optical flow
ECCV '90 Proceedings of the First European Conference on Computer Vision
Egomotion Estimation Using Quadruples of Collinear Image Points
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Multiframe structure from motion in perspective
VSR '95 Proceedings of the IEEE Workshop on Representation of Visual Scenes
Fast processing of image motion patterns arising from 3-D translational motion
Optic flow and beyond
A system for rotational velocity computation from image sequences
Image and Vision Computing
Moving objects detection from time-varied background: an application of camera 3D motion analysis
3DIM'99 Proceedings of the 2nd international conference on 3-D digital imaging and modeling
Focus of expansion localization through inverse C-velocity
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing: Part I
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Presented are two methods for the determination of the parameters of motion of a sensor, given the vector flow field induced by an imaging system governed by a perspective transformation of a rigid scene. Both algorithms integrate global data to determine motion parameters. The first (the flow circulation algorithm) determines the rotational parameters. The second (the FOE search algorithm) determines the translational parameters of the motion independently of the first algorithm. Several methods for determining when the function has the appropriate form are suggested. One method involves filtering the function by a collection of circular-surround zero-mean receptive fields. The other methods project the function onto a linear space of quadratic polynomials and measures the distance between the two functions. The error function for the first two methods is a quadratic polynomial of the candidate position, yielding a very rapid search strategy.