EpicFlow: Edge-Preserving Interpolation of Correspondences for Optical Flow

We propose a novel approach for optical flow estimation , targeted at large displacements with significant oc-clusions. It consists of two steps: i) dense matching by edge-preserving interpolation from a sparse set of matches; ii) variational energy minimization initialized with the dense matches. The sparse-to-dense interpolation relies on an appropriate choice of the distance, namely an edge-aware geodesic distance. This distance is tailored to handle occlusions and motion boundaries -- two common and difficult issues for optical flow computation. We also propose an approximation scheme for the geodesic distance to allow fast computation without loss of performance. Subsequent to the dense interpolation step, standard one-level variational energy minimization is carried out on the dense matches to obtain the final flow estimation. The proposed approach, called Edge-Preserving Interpolation of Correspondences (EpicFlow) is fast and robust to large displacements. It significantly outperforms the state of the art on MPI-Sintel and performs on par on Kitti and Middlebury

SceneFlowFields: Dense Interpolation of Sparse Scene Flow Correspondences

While most scene flow methods use either variational optimization or a strong rigid motion assumption, we show for the first time that scene flow can also be estimated by dense interpolation of sparse matches. To this end, we find sparse matches across two stereo image pairs that are detected without any prior regularization and perform dense interpolation preserving geometric and motion boundaries by using edge information. A few iterations of variational energy minimization are performed to refine our results, which are thoroughly evaluated on the KITTI benchmark and additionally compared to state-of-the-art on MPI Sintel. For application in an automotive context, we further show that an optional ego-motion model helps to boost performance and blends smoothly into our approach to produce a segmentation of the scene into static and dynamic parts.Comment: IEEE Winter Conference on Applications of Computer Vision (WACV), 201

Parameter Inference in Differential Equation Models of Biopathways using Time Warped Gradient Matching

Parameter inference in mechanistic models of biopathways based on systems of coupled differential equations is a topical yet computationally challenging problem, due to the fact that each parameter adaptation involves a numerical integration of the differential equations. Techniques based on gradient matching, which aim to minimize the discrepancy between the slope of a data interpolant and the derivatives predicted from the differential equations, offer a computationally appealing shortcut to the inference problem. However, gradient matching critically hinges on the smoothing scheme for function interpolation, with spurious wiggles in the interpolant having a dramatic effect on the subsequent inference. The present article demonstrates that a time warping approach aiming to homogenize intrinsic functional length scales can lead to a signifi- cant improvement in parameter estimation accuracy. We demonstrate the effectiveness of this scheme on noisy data from a dynamical system with periodic limit cycle and a biopathway

High-speed Video from Asynchronous Camera Array

This paper presents a method for capturing high-speed video using an asynchronous camera array. Our method sequentially fires each sensor in a camera array with a small time offset and assembles captured frames into a high-speed video according to the time stamps. The resulting video, however, suffers from parallax jittering caused by the viewpoint difference among sensors in the camera array. To address this problem, we develop a dedicated novel view synthesis algorithm that transforms the video frames as if they were captured by a single reference sensor. Specifically, for any frame from a non-reference sensor, we find the two temporally neighboring frames captured by the reference sensor. Using these three frames, we render a new frame with the same time stamp as the non-reference frame but from the viewpoint of the reference sensor. Specifically, we segment these frames into super-pixels and then apply local content-preserving warping to warp them to form the new frame. We employ a multi-label Markov Random Field method to blend these warped frames. Our experiments show that our method can produce high-quality and high-speed video of a wide variety of scenes with large parallax, scene dynamics, and camera motion and outperforms several baseline and state-of-the-art approaches.Comment: 10 pages, 82 figures, Published at IEEE WACV 201

A novel disparity-assisted block matching-based approach for super-resolution of light field images

Currently, available plenoptic imaging technology has limited resolution. That makes it challenging to use this technology in applications, where sharpness is essential, such as film industry. Previous attempts aimed at enhancing the spatial resolution of plenoptic light field (LF) images were based on block and patch matching inherited from classical image super-resolution, where multiple views were considered as separate frames. By contrast to these approaches, a novel super-resolution technique is proposed in this paper with a focus on exploiting estimated disparity information to reduce the matching area in the super-resolution process. We estimate the disparity information from the interpolated LR view point images (VPs). We denote our method as light field block matching super-resolution. We additionally combine our novel super-resolution method with directionally adaptive image interpolation from [1] to preserve sharpness of the high-resolution images. We prove a steady gain in the PSNR and SSIM quality of the super-resolved images for the resolution enhancement factor 8x8 as compared to the recent approaches and also to our previous work [2]

Fixation of theoretical ambiguities in the improved fits to xF3xF_3 CCFR data at the next-to-next-to-leading order and beyond

Using the results for the NNLO QCD corrections to anomalous dimensions of odd $xF_3$ Mellin moments and N$^3$LO corrections to their coefficient functions we improve our previous analysis of the CCFR'97 data for $xF_3$. The possibility of extracting from the fits of $1/Q^2$-corrections is analysed using three independent models,including infrared renormalon one. Theoretical quetion of applicability of the renormalon-type inspired large-$\beta_0$ approximation for estimating corrections to the coefficient functions of odd $xF_3$ and even non-singlet $F_2$ moments are considered. The comparison with [1/1] Pad\'e estimates is given. The obtained NLO and NNLO values of $\alpha_s(M_Z)$ are supporting the results of our less definite previous analysis and are in agreement with the world average value $\alpha_s(M_Z)\approx 0.118$. We also present first N$^3$LO extraction of $\alpha_s(M_Z)$. The interplay between higher-order perturbative QCD corrections and $1/Q^2$-terms is demonstrated. The results of our studies are compared with those obtained recently using the NNLO model of the kernel of DGLAP equation and with the results of the NNLO fits to CCFR'97 $xF_3$ data, performed by the Bernstein polynomial technique.Comment: The errors in the coefficients $C_{F_3}^{(3)}(n)$ of ($\alpha_s/4\pi)^3 QCD corrections to the Mellin moments of xF_3 structure function were detected. The application of the corrected results in the fits resulted in decrease of N$^3$LO values of$\Lambda_{\bar{MS}}^{(4)}$ in Tables 6,11,12 by 3 MeV only (details are in the enclosed Erratum (in press)