Nonlinear total variation based noise removal algorithms
Proceedings of the eleventh annual international conference of the Center for Nonlinear Studies on Experimental mathematics : computational issues in nonlinear science: computational issues in nonlinear science
The robust estimation of multiple motions: parametric and piecewise-smooth flow fields
Computer Vision and Image Understanding
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Highly Accurate Optic Flow Computation with Theoretically Justified Warping
International Journal of Computer Vision
Particle Video: Long-Range Motion Estimation Using Point Trajectories
International Journal of Computer Vision
Multi-scale Improves Boundary Detection in Natural Images
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Constraints for the estimation of displacement vector fields from image sequences
IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 2
Image and Vision Computing
Dense, robust, and accurate motion field estimation from stereo image sequences in real-time
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
A high-quality video denoising algorithm based on reliable motion estimation
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
A Database and Evaluation Methodology for Optical Flow
International Journal of Computer Vision
A First-Order Primal-Dual Algorithm for Convex Problems with Applications to Imaging
Journal of Mathematical Imaging and Vision
International Journal of Computer Vision
Bilateral filtering-based optical flow estimation with occlusion detection
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
A multi-scale bilateral structure tensor based corner detector
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
Improving the robustness of variational optical flow through tensor voting
Computer Vision and Image Understanding
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Motion Detail Preserving Optical Flow Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning a Confidence Measure for Optical Flow
IEEE Transactions on Pattern Analysis and Machine Intelligence
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We present a novel combined post-filtering (CPF) method to improve the accuracy of optical flow estimation. Its attractive advantages are that outliers reduction is attained while discontinuities are well preserved, and occlusions are partially handled. Major contributions are the following: First, the structure tensor (ST) based edge detection is introduced to extract flow edges. Moreover, we improve the detection performance by extending the traditional 2D spatial edge detector into spatial-scale 3D space, and also using a gradient bilateral filter (GBF) to replace the linear Gaussian filter to construct a multi-scale nonlinear ST. GBF is useful to preserve discontinuity but it is computationally expensive. A hybrid GBF and Gaussian filter (HGBGF) approach is proposed by means of a spatial-scale gradient signal-to-noise ratio (SNR) measure to solve the low efficiency issue. Additionally, a piecewise occlusion detection method is used to extract occlusions. Second, we apply a CPF method, which uses a weighted median filter (WMF), a bilateral filter (BF) and a fast median filter (MF), to post-smooth the detected edges and occlusions, and the other flat regions of the flow field, respectively. Benchmark tests on both synthetic and real sequences demonstrate the effectiveness of our method.