The Experts below are selected from a list of 315 Experts worldwide ranked by ideXlab platform
Emilio Frazzoli - One of the best experts on this subject based on the ideXlab platform.
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HSCC - Sampling-Based Resolution-Complete Algorithms for Safety Falsification of Linear Systems
Hybrid Systems: Computation and Control, 2008Co-Authors: Amit Bhatia, Emilio FrazzoliAbstract:In this paper, we describe a novel approach for checking safety specifications of a dynamical system with exogenous inputs over infinite time horizon. We introduce the notion of Resolution completeness for analysis of safety falsification algorithms and present sampling-based Resolution-complete algorithms for safety falsification of discrete-time linear time-invariant systems. Given a Target Resolution of inputs, the algorithms terminate either with a reachable state that violates the safety specification, or prove that the system does not violate the specification at the given Resolution of inputs.
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Resolution-complete safety falsification of continuous time systems
Proceedings of the 45th IEEE Conference on Decision and Control, 2006Co-Authors: Amit Bhatia, Emilio FrazzoliAbstract:In this paper we consider a class of analysis problems for control systems, aimed at safety falsification, i.e., checking whether a controlled trajectory exists that violates a given safety property. We introduce a notion of Resolution completeness for safety falsification, and present a Resolution-complete algorithm applicable to continuous-time LTI systems. The algorithm is based on deterministic incremental search procedures, building feasible trajectories exploring the reachable set at increasing Resolution levels. Given a Target Resolution, the algorithm terminates either with a trajectory that violates the safety specification, or proves that no input within a certain class exists that violates the specification
Amit Bhatia - One of the best experts on this subject based on the ideXlab platform.
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HSCC - Sampling-Based Resolution-Complete Algorithms for Safety Falsification of Linear Systems
Hybrid Systems: Computation and Control, 2008Co-Authors: Amit Bhatia, Emilio FrazzoliAbstract:In this paper, we describe a novel approach for checking safety specifications of a dynamical system with exogenous inputs over infinite time horizon. We introduce the notion of Resolution completeness for analysis of safety falsification algorithms and present sampling-based Resolution-complete algorithms for safety falsification of discrete-time linear time-invariant systems. Given a Target Resolution of inputs, the algorithms terminate either with a reachable state that violates the safety specification, or prove that the system does not violate the specification at the given Resolution of inputs.
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Resolution-complete safety falsification of continuous time systems
Proceedings of the 45th IEEE Conference on Decision and Control, 2006Co-Authors: Amit Bhatia, Emilio FrazzoliAbstract:In this paper we consider a class of analysis problems for control systems, aimed at safety falsification, i.e., checking whether a controlled trajectory exists that violates a given safety property. We introduce a notion of Resolution completeness for safety falsification, and present a Resolution-complete algorithm applicable to continuous-time LTI systems. The algorithm is based on deterministic incremental search procedures, building feasible trajectories exploring the reachable set at increasing Resolution levels. Given a Target Resolution, the algorithm terminates either with a trajectory that violates the safety specification, or proves that no input within a certain class exists that violates the specification
Carsten Rother - One of the best experts on this subject based on the ideXlab platform.
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Depth Super Resolution by Rigid Body Self-Similarity in 3D
2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013Co-Authors: Michael Hornácek, Christoph Rhemann, Margrit Gelautz, Carsten RotherAbstract:We tackle the problem of jointly increasing the spatial Resolution and apparent measurement accuracy of an input low-Resolution, noisy, and perhaps heavily quantized depth map. In stark contrast to earlier work, we make no use of ancillary data like a color image at the Target Resolution, multiple aligned depth maps, or a database of high-Resolution depth exemplars. Instead, we proceed by identifying and merging patch correspondences within the input depth map itself, exploiting patch wise scene self-similarity across depth such as repetition of geometric primitives or object symmetry. While the notion of 'single-image' super Resolution has successfully been applied in the context of color and intensity images, we are to our knowledge the first to present a tailored analogue for depth images. Rather than reason in terms of patches of 2D pixels as others have before us, our key contribution is to proceed by reasoning in terms of patches of 3D points, with matched patch pairs related by a respective 6 DoF rigid body motion in 3D. In support of obtaining a dense correspondence field in reasonable time, we introduce a new 3D variant of Patch Match. A third contribution is a simple, yet effective patch up scaling and merging technique, which predicts sharp object boundaries at the Target Resolution. We show that our results are highly competitive with those of alternative techniques leveraging even a color image at the Target Resolution or a database of high-Resolution depth exemplars.
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CVPR - Depth Super Resolution by Rigid Body Self-Similarity in 3D
2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013Co-Authors: Michael Hornácek, Christoph Rhemann, Margrit Gelautz, Carsten RotherAbstract:We tackle the problem of jointly increasing the spatial Resolution and apparent measurement accuracy of an input low-Resolution, noisy, and perhaps heavily quantized depth map. In stark contrast to earlier work, we make no use of ancillary data like a color image at the Target Resolution, multiple aligned depth maps, or a database of high-Resolution depth exemplars. Instead, we proceed by identifying and merging patch correspondences within the input depth map itself, exploiting patch wise scene self-similarity across depth such as repetition of geometric primitives or object symmetry. While the notion of 'single-image' super Resolution has successfully been applied in the context of color and intensity images, we are to our knowledge the first to present a tailored analogue for depth images. Rather than reason in terms of patches of 2D pixels as others have before us, our key contribution is to proceed by reasoning in terms of patches of 3D points, with matched patch pairs related by a respective 6 DoF rigid body motion in 3D. In support of obtaining a dense correspondence field in reasonable time, we introduce a new 3D variant of Patch Match. A third contribution is a simple, yet effective patch up scaling and merging technique, which predicts sharp object boundaries at the Target Resolution. We show that our results are highly competitive with those of alternative techniques leveraging even a color image at the Target Resolution or a database of high-Resolution depth exemplars.
Michael Hornácek - One of the best experts on this subject based on the ideXlab platform.
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Depth Super Resolution by Rigid Body Self-Similarity in 3D
2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013Co-Authors: Michael Hornácek, Christoph Rhemann, Margrit Gelautz, Carsten RotherAbstract:We tackle the problem of jointly increasing the spatial Resolution and apparent measurement accuracy of an input low-Resolution, noisy, and perhaps heavily quantized depth map. In stark contrast to earlier work, we make no use of ancillary data like a color image at the Target Resolution, multiple aligned depth maps, or a database of high-Resolution depth exemplars. Instead, we proceed by identifying and merging patch correspondences within the input depth map itself, exploiting patch wise scene self-similarity across depth such as repetition of geometric primitives or object symmetry. While the notion of 'single-image' super Resolution has successfully been applied in the context of color and intensity images, we are to our knowledge the first to present a tailored analogue for depth images. Rather than reason in terms of patches of 2D pixels as others have before us, our key contribution is to proceed by reasoning in terms of patches of 3D points, with matched patch pairs related by a respective 6 DoF rigid body motion in 3D. In support of obtaining a dense correspondence field in reasonable time, we introduce a new 3D variant of Patch Match. A third contribution is a simple, yet effective patch up scaling and merging technique, which predicts sharp object boundaries at the Target Resolution. We show that our results are highly competitive with those of alternative techniques leveraging even a color image at the Target Resolution or a database of high-Resolution depth exemplars.
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CVPR - Depth Super Resolution by Rigid Body Self-Similarity in 3D
2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013Co-Authors: Michael Hornácek, Christoph Rhemann, Margrit Gelautz, Carsten RotherAbstract:We tackle the problem of jointly increasing the spatial Resolution and apparent measurement accuracy of an input low-Resolution, noisy, and perhaps heavily quantized depth map. In stark contrast to earlier work, we make no use of ancillary data like a color image at the Target Resolution, multiple aligned depth maps, or a database of high-Resolution depth exemplars. Instead, we proceed by identifying and merging patch correspondences within the input depth map itself, exploiting patch wise scene self-similarity across depth such as repetition of geometric primitives or object symmetry. While the notion of 'single-image' super Resolution has successfully been applied in the context of color and intensity images, we are to our knowledge the first to present a tailored analogue for depth images. Rather than reason in terms of patches of 2D pixels as others have before us, our key contribution is to proceed by reasoning in terms of patches of 3D points, with matched patch pairs related by a respective 6 DoF rigid body motion in 3D. In support of obtaining a dense correspondence field in reasonable time, we introduce a new 3D variant of Patch Match. A third contribution is a simple, yet effective patch up scaling and merging technique, which predicts sharp object boundaries at the Target Resolution. We show that our results are highly competitive with those of alternative techniques leveraging even a color image at the Target Resolution or a database of high-Resolution depth exemplars.
U. Spagnolini - One of the best experts on this subject based on the ideXlab platform.
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MultiTarget detection/tracking of echoes with known waveform: algorithm and applications
1997 IEEE International Conference on Acoustics Speech and Signal Processing, 1997Co-Authors: V. Rampa, U. SpagnoliniAbstract:The time of delay (TOD) estimation of multiple echoes is solved with an iterative multiTarget detection/tracking algorithm. The evaluation of the TODs is based on their a-posteriori probability, while a first-order Markov model is used for a-priori probability estimation. The effectiveness of the algorithm (low false-alarm rate and robustness) is also experimentally proven. Moreover the algorithm exhibits a better noise rejection and an improved Target Resolution with respect to algorithms that perform separate detection and tracking.
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ICASSP - MultiTarget detection/tracking of echoes with known waveform: algorithm and applications
1997 IEEE International Conference on Acoustics Speech and Signal Processing, 1997Co-Authors: V. Rampa, U. SpagnoliniAbstract:The time of delay (TOD) estimation of multiple echoes is solved with an iterative multiTarget detection/tracking algorithm. The evaluation of the TODs is based on their a-posteriori probability, while a first-order Markov model is used for a-priori probability estimation. The effectiveness of the algorithm (low false-alarm rate and robustness) is also experimentally proven. Moreover the algorithm exhibits a better noise rejection and an improved Target Resolution with respect to algorithms that perform separate detection and tracking.
