The Experts below are selected from a list of 43617 Experts worldwide ranked by ideXlab platform
Christoph S. Garbe - One of the best experts on this subject based on the ideXlab platform.
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a statistical confidence measure for Optical Flows
European Conference on Computer Vision, 2008Co-Authors: Claudia Kondermann, Rudolf Mester, Christoph S. GarbeAbstract:Confidence measures are crucial to the interpretation of any Optical flow measurement. Even though numerous methods for estimating Optical flow have been proposed over the last three decades, a sound, universal, and statistically motivated confidence measure for Optical flow measurements is still missing. We aim at filling this gap with this contribution, where such a confidence measure is derived, using statistical test theory and measurable statistics of flow fields from the regarded domain. The new confidence measure is computed from merely the results of the Optical flow estimator and hence can be applied to any Optical flow estimation method, covering the range from local parametric to global variational approaches. Experimental results using state-of-the-art Optical flow estimators and various test sequences demonstrate the superiority of the proposed technique compared to existing 'confidence' measures.
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ECCV (3) - A Statistical Confidence Measure for Optical Flows
Lecture Notes in Computer Science, 2008Co-Authors: Claudia Kondermann, Rudolf Mester, Christoph S. GarbeAbstract:Confidence measures are crucial to the interpretation of any Optical flow measurement. Even though numerous methods for estimating Optical flow have been proposed over the last three decades, a sound, universal, and statistically motivated confidence measure for Optical flow measurements is still missing. We aim at filling this gap with this contribution, where such a confidence measure is derived, using statistical test theory and measurable statistics of flow fields from the regarded domain. The new confidence measure is computed from merely the results of the Optical flow estimator and hence can be applied to any Optical flow estimation method, covering the range from local parametric to global variational approaches. Experimental results using state-of-the-art Optical flow estimators and various test sequences demonstrate the superiority of the proposed technique compared to existing 'confidence' measures.
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an adaptive confidence measure for Optical Flows based on linear subspace projections
DAGM conference on Pattern Recognition, 2007Co-Authors: Claudia Kondermann, Daniel Kondermann, Bernd Jähne, Christoph S. GarbeAbstract:Confidence measures are important for the validation of Optical flow fields by estimating the correctness of each displacement vector. There are several frequently used confidence measures, which have been found of at best intermediate quality. Hence, we propose a new confidence measure based on linear subspace projections. The results are compared to the best previously proposed confidence measures with respect to an optimal confidence. Using the proposed measure we are able to improve previous results by up to 31%.
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DAGM-Symposium - An adaptive confidence measure for Optical Flows based on linear subspace projections
Lecture Notes in Computer Science, 2007Co-Authors: Claudia Kondermann, Daniel Kondermann, Bernd Jähne, Christoph S. GarbeAbstract:Confidence measures are important for the validation of Optical flow fields by estimating the correctness of each displacement vector. There are several frequently used confidence measures, which have been found of at best intermediate quality. Hence, we propose a new confidence measure based on linear subspace projections. The results are compared to the best previously proposed confidence measures with respect to an optimal confidence. Using the proposed measure we are able to improve previous results by up to 31%.
Claudia Kondermann - One of the best experts on this subject based on the ideXlab platform.
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Postprocessing and Restoration of Optical Flows
2009Co-Authors: Claudia KondermannAbstract:The notion "Optical flow" refers to the apparent motion in the image plane produced by the projection of the real 3D motion onto the 2D image plane. The thesis at hand addresses postprocessing and restoration methods for arbitrarily computed Optical flow fields. Many motion estimators have been proposed during the last three decades, but all of them suffer from shortcomings in difficult situations. Hence, it is of utmost importance for any Optical flow measurement technique to give a prediction of the quality and reliability of each individual flow vector. Yet, a sound, universally applicable, and statistically motivated confidence measure for Optical flow measurements is still missing today. Based on such information, erroneous Optical flow fields can be restored or improved by means of inpainting techniques. This thesis introduces three confidence measures, which evaluate the reliability of Optical flow vectors. In contrast to previously employed methods, these confidence measures are based on learned motion models and are, thus, statistically motivated, they are independent of the original flow computation method and yield more accurate predictions on the quality of Optical flow vectors. The thesis puts a second focus on the restoration of Optical flow fields, where it transfers inpainting techniques from the restoration of images to the field of motion recovery. Since the reconstruction process in case of motion fields can use the image sequence as additional source of information, a novel motion inpainting approach is proposed. It combines motion and image information in one functional and, thus, allows to control the orientation of the reconstruction algorithm based on image edges.
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a statistical confidence measure for Optical Flows
European Conference on Computer Vision, 2008Co-Authors: Claudia Kondermann, Rudolf Mester, Christoph S. GarbeAbstract:Confidence measures are crucial to the interpretation of any Optical flow measurement. Even though numerous methods for estimating Optical flow have been proposed over the last three decades, a sound, universal, and statistically motivated confidence measure for Optical flow measurements is still missing. We aim at filling this gap with this contribution, where such a confidence measure is derived, using statistical test theory and measurable statistics of flow fields from the regarded domain. The new confidence measure is computed from merely the results of the Optical flow estimator and hence can be applied to any Optical flow estimation method, covering the range from local parametric to global variational approaches. Experimental results using state-of-the-art Optical flow estimators and various test sequences demonstrate the superiority of the proposed technique compared to existing 'confidence' measures.
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ECCV (3) - A Statistical Confidence Measure for Optical Flows
Lecture Notes in Computer Science, 2008Co-Authors: Claudia Kondermann, Rudolf Mester, Christoph S. GarbeAbstract:Confidence measures are crucial to the interpretation of any Optical flow measurement. Even though numerous methods for estimating Optical flow have been proposed over the last three decades, a sound, universal, and statistically motivated confidence measure for Optical flow measurements is still missing. We aim at filling this gap with this contribution, where such a confidence measure is derived, using statistical test theory and measurable statistics of flow fields from the regarded domain. The new confidence measure is computed from merely the results of the Optical flow estimator and hence can be applied to any Optical flow estimation method, covering the range from local parametric to global variational approaches. Experimental results using state-of-the-art Optical flow estimators and various test sequences demonstrate the superiority of the proposed technique compared to existing 'confidence' measures.
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an adaptive confidence measure for Optical Flows based on linear subspace projections
DAGM conference on Pattern Recognition, 2007Co-Authors: Claudia Kondermann, Daniel Kondermann, Bernd Jähne, Christoph S. GarbeAbstract:Confidence measures are important for the validation of Optical flow fields by estimating the correctness of each displacement vector. There are several frequently used confidence measures, which have been found of at best intermediate quality. Hence, we propose a new confidence measure based on linear subspace projections. The results are compared to the best previously proposed confidence measures with respect to an optimal confidence. Using the proposed measure we are able to improve previous results by up to 31%.
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DAGM-Symposium - An adaptive confidence measure for Optical Flows based on linear subspace projections
Lecture Notes in Computer Science, 2007Co-Authors: Claudia Kondermann, Daniel Kondermann, Bernd Jähne, Christoph S. GarbeAbstract:Confidence measures are important for the validation of Optical flow fields by estimating the correctness of each displacement vector. There are several frequently used confidence measures, which have been found of at best intermediate quality. Hence, we propose a new confidence measure based on linear subspace projections. The results are compared to the best previously proposed confidence measures with respect to an optimal confidence. Using the proposed measure we are able to improve previous results by up to 31%.
Rudolf Mester - One of the best experts on this subject based on the ideXlab platform.
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a statistical confidence measure for Optical Flows
European Conference on Computer Vision, 2008Co-Authors: Claudia Kondermann, Rudolf Mester, Christoph S. GarbeAbstract:Confidence measures are crucial to the interpretation of any Optical flow measurement. Even though numerous methods for estimating Optical flow have been proposed over the last three decades, a sound, universal, and statistically motivated confidence measure for Optical flow measurements is still missing. We aim at filling this gap with this contribution, where such a confidence measure is derived, using statistical test theory and measurable statistics of flow fields from the regarded domain. The new confidence measure is computed from merely the results of the Optical flow estimator and hence can be applied to any Optical flow estimation method, covering the range from local parametric to global variational approaches. Experimental results using state-of-the-art Optical flow estimators and various test sequences demonstrate the superiority of the proposed technique compared to existing 'confidence' measures.
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ECCV (3) - A Statistical Confidence Measure for Optical Flows
Lecture Notes in Computer Science, 2008Co-Authors: Claudia Kondermann, Rudolf Mester, Christoph S. GarbeAbstract:Confidence measures are crucial to the interpretation of any Optical flow measurement. Even though numerous methods for estimating Optical flow have been proposed over the last three decades, a sound, universal, and statistically motivated confidence measure for Optical flow measurements is still missing. We aim at filling this gap with this contribution, where such a confidence measure is derived, using statistical test theory and measurable statistics of flow fields from the regarded domain. The new confidence measure is computed from merely the results of the Optical flow estimator and hence can be applied to any Optical flow estimation method, covering the range from local parametric to global variational approaches. Experimental results using state-of-the-art Optical flow estimators and various test sequences demonstrate the superiority of the proposed technique compared to existing 'confidence' measures.
Shankar Sastry - One of the best experts on this subject based on the ideXlab platform.
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multibody motion estimation and segmentation from multiple central panoramic views
International Conference on Robotics and Automation, 2003Co-Authors: O Shakernia, Rene Vidal, Shankar SastryAbstract:We present an algorithm for infinitesimal motion estimation and segmentation from multiple central panoramic views. We first show that the central panoramic Optical Flows corresponding to independent motions lie in orthogonal ten-dimensional subspaces of a higher-dimensional linear space. We then propose a factorization-based technique that estimates the number of independent motions, the segmentation of the image measurements and the motion of each object relative to the camera from a set of image points and their Optical Flows in multiple frames. Finally, we present the experimental results on motion estimation and segmentation for a real image sequence with two independently moving mobile robots, and evaluate the performance of our algorithm by comparing the vision estimates with GPS measurements gathered by the mobile robots.
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formation control of nonholonomic mobile robots with omnidirectional visual servoing and motion segmentation
International Conference on Robotics and Automation, 2003Co-Authors: Rene Vidal, O Shakernia, Shankar SastryAbstract:We consider the problem of having a team of nonholonomic mobile robots follow a desired leader-follower formation using omnidirectional vision. By specifying the desired formation in the image plane, we translate the control problem into a separate visual servoing task for each follower. We use a rank constraint on the omnidirectional Optical Flows across multiple frames to estimate the position and velocities of the leaders in the image plane of each follower. We show that the direct feedback-linearization of the leader-follower dynamics suffers from degenerate configurations due to the nonholonomic constraints of the robots and the nonlinearity of the omnidirectional projection model. We therefore design a nonlinear tracking controller that avoids such degenerate configurations, while preserving the formation input-to-state stability. Our control law naturally incorporates collision avoidance by exploiting the geometry of omnidirectional cameras. We present simulations and experiments evaluating our omnidirectional vision-based formation control scheme.
Erik Learnedmiller - One of the best experts on this subject based on the ideXlab platform.
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super slomo high quality estimation of multiple intermediate frames for video interpolation
arXiv: Computer Vision and Pattern Recognition, 2017Co-Authors: Huaizu Jiang, Erik Learnedmiller, Varun Jampani, Minghsuan Yang, Jan KautzAbstract:Given two consecutive frames, video interpolation aims at generating intermediate frame(s) to form both spatially and temporally coherent video sequences. While most existing methods focus on single-frame interpolation, we propose an end-to-end convolutional neural network for variable-length multi-frame video interpolation, where the motion interpretation and occlusion reasoning are jointly modeled. We start by computing bi-directional Optical flow between the input images using a U-Net architecture. These Flows are then linearly combined at each time step to approximate the intermediate bi-directional Optical Flows. These approximate Flows, however, only work well in locally smooth regions and produce artifacts around motion boundaries. To address this shortcoming, we employ another U-Net to refine the approximated flow and also predict soft visibility maps. Finally, the two input images are warped and linearly fused to form each intermediate frame. By applying the visibility maps to the warped images before fusion, we exclude the contribution of occluded pixels to the interpolated intermediate frame to avoid artifacts. Since none of our learned network parameters are time-dependent, our approach is able to produce as many intermediate frames as needed. We use 1,132 video clips with 240-fps, containing 300K individual video frames, to train our network. Experimental results on several datasets, predicting different numbers of interpolated frames, demonstrate that our approach performs consistently better than existing methods.
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coherent motion segmentation in moving camera videos using Optical flow orientations
International Conference on Computer Vision, 2013Co-Authors: Manjunath Narayana, Allen R Hanson, Erik LearnedmillerAbstract:In moving camera videos, motion segmentation is commonly performed using the image plane motion of pixels, or Optical flow. However, objects that are at different depths from the camera can exhibit different Optical Flows even if they share the same real-world motion. This can cause a depth-dependent segmentation of the scene. Our goal is to develop a segmentation algorithm that clusters pixels that have similar real-world motion irrespective of their depth in the scene. Our solution uses Optical flow orientations instead of the complete vectors and exploits the well-known property that under camera translation, Optical flow orientations are independent of object depth. We introduce a probabilistic model that automatically estimates the number of observed independent motions and results in a labeling that is consistent with real-world motion in the scene. The result of our system is that static objects are correctly identified as one segment, even if they are at different depths. Color features and information from previous frames in the video sequence are used to correct occasional errors due to the orientation-based segmentation. We present results on more than thirty videos from different benchmarks. The system is particularly robust on complex background scenes containing objects at significantly different depths.