Estimation of Object Motion Parameters from Noisy Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
3-D motion estimation, understanding, and prediction from nosiy image sequences
IEEE Transactions on Pattern Analysis and Machine Intelligence
3D positional integration from image sequences
Image and Vision Computing
The feasibility of motion and structure from noisy time-varying image velocity information
International Journal of Computer Vision
A multi-frame approach to visual motion perception
International Journal of Computer Vision
Three-dimensional motion computation and object segmentation in a long sequence of stereo frames
International Journal of Computer Vision
Subspace methods for recovering rigid motion I: algorithm and implementation
International Journal of Computer Vision
IAPR Proceedings of the international workshop on Visual form: analysis and recognition
Performance of optical flow techniques
International Journal of Computer Vision
Recursive-batch estimation of motion and structure from monocular image sequences
CVGIP: Image Understanding
The use of optical flow for the autonomous navigation
ECCV '94 Proceedings of the third European conference on Computer vision (vol. 1)
A paraperspective factorization method for shape and motion recovery
ECCV '94 Proceedings of the third European conference on Computer Vision (Vol. II)
The Accuracy of the Computation of Optical Flow and of the Recovery of Motion Parameters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Vehicle-Type Motion Estimation From Multi-Frame Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Motion and Structure Factorization and Segmentation of Long Multiple Motion Image Sequences
ECCV '92 Proceedings of the Second European Conference on Computer Vision
Motion and structure from optical flow
IJCAI'79 Proceedings of the 6th international joint conference on Artificial intelligence - Volume 2
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
The computation of optical flow
ACM Computing Surveys (CSUR)
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol.2)-Volume 2 - Volume 2
Acquisition of translational motion by the parallel trinocular
Information Sciences: an International Journal
Performance characterization in computer vision: A guide to best practices
Computer Vision and Image Understanding
Human activity localization via sequential change detection
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Servo tracking of three-dimensional motion by the parallel trinocular
WSEAS Transactions on Systems and Control
Quantitative depth recovery from time-varying optical flow in a Kalman filter framework
Proceedings of the 11th international conference on Theoretical foundations of computer vision
Hi-index | 0.02 |
We present a computational framework for recovering both first-order motion parameters (observer direction of translation and observer rotation), second-order motion parameters (observer rotational acceleration) and relative depth maps from time-varying optical flow. We recover translation speed and acceleration in units which are scaled relative to the distance to the object. Our assumption is that the observer rotational motion is no more than ''second order'', in other words, observer motion is either constant or has at most constant acceleration. We examine the effect of noise on the solution of the motion and structure parameters. This ensemble of unknowns comprises a solution to the classical ''structure-and-motion from optic flow'' problem. Our complete framework utilizes a method for interpreting the bilinear image velocity equation by solving simple systems of linear equations. Since our noise analysis yields uncertainty measures for each parameter, a Kalman filter is employed to incrementally integrate new measurements as they become available as each additional frame in the sequence is processed. We conclude by analysing this reduction of uncertainty over time as the system converges to a stable solution for both synthetic and real image sequences.