IEEE Transactions on Pattern Analysis and Machine Intelligence
Multidimensional Orientation Estimation with Applications to Texture Analysis and Optical Flow
IEEE Transactions on Pattern Analysis and Machine Intelligence
Performance of optical flow techniques
International Journal of Computer Vision
A Theoretical Framework for Convex Regularizers in PDE-Based Computation of Image Motion
International Journal of Computer Vision
Lucas/Kanade meets Horn/Schunck: combining local and global optic flow methods
International Journal of Computer Vision
A Statistical Confidence Measure for Optical Flows
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Numerical Mathematics and Computing
Numerical Mathematics and Computing
Beyond pixels: exploring new representations and applications for motion analysis
Beyond pixels: exploring new representations and applications for motion analysis
A Database and Evaluation Methodology for Optical Flow
International Journal of Computer Vision
When is a confidence measure good enough?
ICVS'13 Proceedings of the 9th international conference on Computer Vision Systems
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Assessing the performance of optical flow in the absence of ground truth is of prime importance for a correct interpretation and application. Thus, in recent years, the interest in developing confidence measures has increased. However, by its complexity, assessing the capability of such measures for detecting areas of poor performance of optical flow is still unsolved. We define a confidence measure in the context of numerical stability of the optical flow scheme and also a protocol for assessing its capability to discard areas of non-reliable flows. Results on the Middlebury database validate our framework and show that, unlike existing measures, our measure is not biased towards any particular image feature.