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Optical flow methods are used to estimate pixelwise motion information based on consecutive frames in image sequences. The image sequences traditionally contain frames that are similarly exposed. However, many real-world scenes contain high dynamic range content that cannot be captured well with a single exposure setting. Such scenes result in certain image regions being over- or underexposed, which can negatively impact the quality of motion estimates in those regions. Motivated by this, we propose to capture high dynamic range scenes using different exposure settings every other frame. A framework for OF estimation on such image sequences is presented, that can straightforwardly integrate techniques from the state-of-the-art in conventional OF methods. Different aspects of robustness of OF methods are discussed, including estimation of large displacements and robustness to natural illumination changes that occur between the frames, and we demonstrate experimentally how to handle such challenging flow estimation scenarios. The flow estimation is formulated as an optimization problem whose solution is obtained using an efficient primal–dual method. [ABSTRACT FROM AUTHOR]

Abstract: We propose a scheme for comparing local neighborhoods (window) of image points, to estimate optical flow using discrete optimization. The proposed approach is based on using large correlation windows with adaptive support-weights. We present three new types of weighting constraints derived from image gradient, color statistics and occlusion information. The first type provides gradient structure constraints that favor flow consistency across strong image gradients. The second type imposes perceptual color constraints that reinforce relationship among pixels in a window according to their color statistics. The third type yields occlusion constraints that reject pixels that are seen in one window but not seen in the other. All these constraints contribute to suppress the effect of cluttered background, which is unavoidably included in the large correlation windows. Experimental results demonstrate that each of the proposed constraints appreciably elevates the quality of estimations, and that they jointly yield results that compare favorably to current techniques, especially on object boundaries. [Copyright &y& Elsevier]

Abstract: Many applications in computer vision and computer graphics require dense correspondences between images of multi-view video streams. Most state-of-the-art algorithms estimate correspondences by considering pairs of images. However, in multi-view videos, several images capture nearly the same scene. In this article we show that this redundancy can be exploited to estimate more robust and consistent correspondence fields. We use the multi-video data structure to establish a confidence measure based on the consistency of the correspondences in a loop of three images. This confidence measure can be applied after flow estimation is terminated to find the pixels for which the estimate is reliable. However, including the measure directly into the estimation process yields dense and highly accurate correspondence fields. Additionally, application of the loop consistency confidence measure allows us to include sparse feature matches directly into the dense optical flow estimation. With the confidence measure, spurious matches can be successfully suppressed during optical flow estimation while correct matches contribute to increase the accuracy of the flow. [Copyright &y& Elsevier]

Abstract: In this paper, we present a method for human full-body pose estimation from depth data that can be obtained using Time of Flight (ToF) cameras or the Kinect device. Our approach consists of robustly detecting anatomical landmarks in the 3D data and fitting a skeleton body model using constrained inverse kinematics. Instead of relying on appearance-based features for interest point detection that can vary strongly with illumination and pose changes, we build upon a graph-based representation of the depth data that allows us to measure geodesic distances between body parts. As these distances do not change with body movement, we are able to localize anatomical landmarks independent of pose. For differentiation of body parts that occlude each other, we employ motion information, obtained from the optical flow between subsequent intensity images. We provide a qualitative and quantitative evaluation of our pose tracking method on ToF and Kinect sequences containing movements of varying complexity. [Copyright &y& Elsevier]

Abstract: We introduce a novel video stabilization method that enables the extraction of optic flow from short unstable sequences. Contrary to traditional stabilization techniques that use approximative global motion models to estimate the full camera motion, our method estimates the unstable component of the camera motion only. This allows for the use of simpler global motion models, and at the same time extends the validity to more complex environments, such as close scenes that contain independently moving objects. The unstable component of the camera motion is derived from a maximization of the temporal local velocity constancy over the entire short sequence. The method, embedded within a phase-based optic flow algorithm, is tested on both synthetic and complex real-world sequences. The optic flow obtained using our technique is denser than that extracted directly from the original sequence, and from a sequence stabilized with a more traditional stabilization technique. [Copyright &y& Elsevier]

A fast method for accurate measurement of displacement field that combines template matching and differential techniques is proposed. This method, which is able to deal with the problems of non-rigid object movement as well as large inter frame displacements, operates in three steps. First, a sparse displacement field is obtained by a classic template matching technique. Then, this information is propagated through the overall image to obtain a dense displacement field. Finally, this field is considered as approximate solution before the use of classical differential technique for optical flow estimation. To illustrate the efficiency of the proposed procedure, a number of experimental results using both synthetic and real images are presented and discussed. The results show significant improvement over those obtained using standard multigrid approaches, especially in the cases of textured, very structured images or large inter frame displacements. [Copyright &y& Elsevier]