Biological Cybernetics
A simplified linear optic-flow motion algorithm
Computer Vision, Graphics, and Image Processing
Visual perception of three-dimensional motion
Neural Computation
Subspace methods for recovering rigid motion I: algorithm and implementation
International Journal of Computer Vision
3-D interpretation of optical flow by renormalization
International Journal of Computer Vision
The role of fixation in visual motion analysis
International Journal of Computer Vision
Performance of optical flow techniques
International Journal of Computer Vision
Optical flow estimation: advances and comparisons
ECCV '94 Proceedings of the third European conference on Computer vision (vol. 1)
Computing egomotion and detecting independent motion from image motion using colinear points
Computer Vision and Image Understanding
In Defense of the Eight-Point Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recovering 3D Motion of Multiple Objects Using Adaptive Hough Transform
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimal Structure from Motion: Local Ambiguities and Global Estimates
International Journal of Computer Vision
Theory of Reconstruction from Image Motion
Theory of Reconstruction from Image Motion
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
On the consistency of instantaneous rigid motion estimation
International Journal of Computer Vision
Multi Viewpoint Stereo from Uncalibrated Video Sequences
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Motion Recovery from Image Sequences: Discrete Viewpoint vs. Differential Viewpoint
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Comparison of Approaches to Egomotion Computation
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Occlusion Detectable Stereo -- Occlusion Patterns in Camera Matrix
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Removal of Translation Bias when Using Subspace Methods
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Direct Estimation of Structure and Motion from Multiple Frames
Direct Estimation of Structure and Motion from Multiple Frames
Revisiting Hartley's Normalized Eight-Point Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimal instantaneous rigid motion estimation insensitive to local minima
Computer Vision and Image Understanding
An Efficient Linear Method for the Estimation of Ego-Motion from Optical Flow
Proceedings of the 31st DAGM Symposium on Pattern Recognition
Fundamentals of Computer Graphics
Fundamentals of Computer Graphics
A probabilistic framework for correspondence and egomotion
WDV'05/WDV'06/ICCV'05/ECCV'06 Proceedings of the 2005/2006 international conference on Dynamical vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stereo by Intra- and Inter-Scanline Search Using Dynamic Programming
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adjustable linear models for optic flow based obstacle avoidance
Computer Vision and Image Understanding
High frame rate egomotion estimation
ICVS'13 Proceedings of the 9th international conference on Computer Vision Systems
Hi-index | 0.00 |
If a visual observer moves through an environment, the patterns of light that impinge its retina vary leading to changes in sensed brightness. Spatial shifts of brightness patterns in the 2D image over time are called optic flow. In contrast to optic flow visual motion fields denote the displacement of 3D scene points projected onto the camera's sensor surface. For translational and rotational movement through a rigid scene parametric models of visual motion fields have been defined. Besides ego-motion these models provide access to relative depth, and both ego-motion and depth information is useful for visual navigation. In the past 30 years methods for ego-motion estimation based on models of visual motion fields have been developed. In this review we identify five core optimization constraints which are used by 13 methods together with different optimization techniques. In the literature methods for ego-motion estimation typically have been evaluated by using an error measure which tests only a specific ego-motion. Furthermore, most simulation studies used only a Gaussian noise model. Unlike, we test multiple types and instances of ego-motion. One type is a fixating ego-motion, another type is a curve-linear ego-motion. Based on simulations we study properties like statistical bias, consistency, variability of depths, and the robustness of the methods with respect to a Gaussian or outlier noise model. In order to achieve an improvement of estimates for noisy visual motion fields, part of the 13 methods are combined with techniques for robust estimation like m-functions or RANSAC. Furthermore, a realistic scenario of a stereo image sequence has been generated and used to evaluate methods of ego-motion estimation provided by estimated optic flow and depth information.