The Experts below are selected from a list of 22497 Experts worldwide ranked by ideXlab platform
Joris De Schutter - One of the best experts on this subject based on the ideXlab platform.
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comparison of Rigid Body Motion trajectory descriptors for Motion representation and recognition
International Conference on Robotics and Automation, 2015Co-Authors: Maxim Vochten, Tinne De Laet, Joris De SchutterAbstract:This paper presents an overview and comparison of minimal and complete Rigid Body Motion trajectory descriptors, usable in applications like Motion recognition and programming by demonstration. Motion trajectory descriptors are able to deal with potentially unwanted variations acting on the Motion trajectory such as changes in the execution time, the Motion's starting position, or the viewpoint from which the Motion is observed. A suitable Rigid Body Motion trajectory descriptor retains only the trajectory information relevant to the application. This paper compares different trajectory descriptors for Rigid Body Motion and validates their usefulness for dealing with Motion variation in a Motion recognition experiment. Furthermore, a new type of invariant trajectory descriptor is introduced based on the Frenet-Serret formulas.
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ICRA - Comparison of Rigid Body Motion trajectory descriptors for Motion representation and recognition
2015 IEEE International Conference on Robotics and Automation (ICRA), 2015Co-Authors: Maxim Vochten, Tinne De Laet, Joris De SchutterAbstract:This paper presents an overview and comparison of minimal and complete Rigid Body Motion trajectory descriptors, usable in applications like Motion recognition and programming by demonstration. Motion trajectory descriptors are able to deal with potentially unwanted variations acting on the Motion trajectory such as changes in the execution time, the Motion's starting position, or the viewpoint from which the Motion is observed. A suitable Rigid Body Motion trajectory descriptor retains only the trajectory information relevant to the application. This paper compares different trajectory descriptors for Rigid Body Motion and validates their usefulness for dealing with Motion variation in a Motion recognition experiment. Furthermore, a new type of invariant trajectory descriptor is introduced based on the Frenet-Serret formulas.
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ASCC - Classical and subsequence dynamic time warping for recognition of Rigid Body Motion trajectories
2013 9th Asian Control Conference (ASCC), 2013Co-Authors: Ozkan Cigdem, Tinne De Laet, Joris De SchutterAbstract:In many robotic applications, the Motions of a human and a robot are recognized by studying of their Motion trajectories. Hence, Motion trajectory recognition is important in human and robot movement analysis. In this paper, the recognition of six degrees-of-freedom Rigid Body Motion trajectory of an object is studied. The three-dimensional measured position trajectories of LED markers attached to Rigid Body are transformed to the time-based invariant representation of the Rigid Body Motion trajectories. The main objective of this paper is to evaluate the performance of classical and subsequence dynamic time warping algorithm on the recognition of the Rigid Body Motion trajectories. The experimental results show that the use of the length of shortest warping path in the calculation of DTW distances is not significantly better than the use of only the length of model signal for the nine artificial Motions used in experiments. However, the used method promises to improve recognition of more complex everyday Motions. Additionally, the results indicate that the classical DTW algorithm gives more meaningful results than subsequence DTW algorithm for the available recorded Motions.
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SMC - Invariant representations to reduce the variability in recognition of Rigid Body Motion trajectories
2012 IEEE International Conference on Systems Man and Cybernetics (SMC), 2012Co-Authors: Tjorven Delabie, Tinne De Laet, Ozkan Cigdem, Jochem F.m. De Schutter, Roel Matthysen, Joris De SchutterAbstract:In this paper, the use of a coordinate-free representation for recognizing six DOF Rigid Body Motion trajectories is experimentally validated. In the recognition part of this approach, the three-dimensional measured position trajectories of arbitrary and uncalibrated points attached to the Rigid Body are transformed to an invariant, coordinate-free representation of the Rigid Body Motion trajectory. This representation is theoretically independent of the reference frame in which the Motion is observed, the chosen marker positions, the linear scale (magnitude) of the Motion, the time scale, and the Motion profile. During the experiments, a person manipulated an object. The camera viewpoints, time scales, Motions profiles, and linear or angular scales were changed between different Motion recordings. The experimental results validate that not only in similar but also in different recording conditions, through using the invariant representation, the dependency on the parameters mentioned above are eliminated, and therefore better recognition results are obtained.
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Recognition of 6 DOF Rigid Body Motion trajectories using a coordinate-free representation
2011 IEEE International Conference on Robotics and Automation, 2011Co-Authors: Joris De Schutter, Jochem F.m. De Schutter, Roel Matthysen, Enrico Di Lello, Tuur Benoit, Tinne De LaetAbstract:This paper presents an approach to recognize 6 DOF Rigid Body Motion trajectories (3D translation + rotation), such as the 6 DOF Motion trajectory of an object manipulated by a human. As a first step in the recognition process, 3D measured position trajectories of arbitrary and uncalibrated points attached to the Rigid Body are transformed to an invariant, coordinate-free representation of the Rigid Body Motion trajectory. This invariant representation is independent of the reference frame in which the Motion is observed, the chosen marker positions, the linear scale (magnitude) of the Motion, the time scale and the velocity profile along the trajectory. Two classification algorithms which use the invariant representation as input are developed and tested experimentally: one approach based on a Dynamic Time Warping algorithm, and one based on Hidden Markov Models. Both approaches yield high recognition rates (up to 95 % and 91 %, respectively). The advantage of the invariant approach is that Motion trajectories observed in different contexts (with different reference frames, marker positions, time scales, linear scales, velocity profiles) can be compared and averaged, which allows us to build models from multiple demonstrations observed in different contexts, and use these models to recognize similar Motion trajectories in still different contexts.
Zhanpeng Shao - One of the best experts on this subject based on the ideXlab platform.
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RRV: A Spatiotemporal Descriptor for Rigid Body Motion Recognition
IEEE Transactions on Cybernetics, 2017Co-Authors: Yao Guo, Youfu Li, Zhanpeng ShaoAbstract:The Motion behaviors of a Rigid Body can be characterized by a six degrees of freedom Motion trajectory, which contains the 3-D position vectors of a reference point on the Rigid Body and 3-D rotations of this Rigid Body over time. This paper devises a rotation and relative velocity (RRV) descriptor by exploring the local translational and rotational invariants of Rigid Body Motion trajectories, which is insensitive to noise, invariant to Rigid transformation and scale. The RRV descriptor is then applied to characterize Motions of a human Body skeleton modeled as articulated interconnections of multiple Rigid bodies. To show the descriptive ability of our RRV descriptor, we explore its potentials and applications in different Rigid Body Motion recognition tasks. The experimental results on benchmark datasets demonstrate that our RRV descriptor learning discriminative Motion patterns can achieve superior results for various recognition tasks.
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IROS - MSM-HOG: A flexible trajectory descriptor for Rigid Body Motion recognition
2017 IEEE RSJ International Conference on Intelligent Robots and Systems (IROS), 2017Co-Authors: Yao Guo, Zhanpeng ShaoAbstract:This paper proposes a flexible descriptor for representing 6-D Rigid Body Motion trajectories, which not only shows strong invariances and descriptive ability but also achieves satisfactory results in both recognition accuracy and efficiency. 6-D Rigid Body Motion trajectories are first transformed into the Multi-layer Self-similarity Matrices (MSM) representation. The MSM is the combination of the square similarity matrices in three layers, which captures both local and global spatiotemporal features of the trajectories. Next, the Histogram of Oriented Gradients (HOG) features extracted from the MSM representation are concatenated as the final MSM-HOG trajectory descriptor. Then we train the Support Vector Machine (SVM) classifier with the linear kernel for multicalss Motion recognition. Finally, Rigid Body Motion recognition experiments on two public datasets are conducted to verify the effectiveness and efficiency of the proposed method.
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DSRF: A flexible descriptor for effective Rigid Body Motion trajectory recognition
2016 IEEE International Conference on Mechatronics and Automation, 2016Co-Authors: Yao Guo, Zhanpeng ShaoAbstract:Rigid Body Motion trajectories can provide sufficient clues in understanding Motion behaviors of objects of interest. An invariant descriptor for a Motion trajectory can offer substantial advantages over raw data. This paper firstly proposes a Dual Square-Root Function (DSRF) descriptor by only calculating gradient-based shape features of normalized Rigid Body Motion trajectories, while high-order time derivatives are involved in previous works. Our DSRF descriptor has shown richness in description, moreover, it is invariant to scaling, Rigid transformation, robust to noise and beneficial for matching rate-variance trajectories. To illustrate these, we then evaluate DSRF descriptor for different trajectory-based Rigid Body Motion recognition tasks. Experimental results on two benchmark datasets demonstrate that it outperforms previous ones in terms of the recognition accuracy and robustness.
Anders M Dale - One of the best experts on this subject based on the ideXlab platform.
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Real-time Rigid Body Motion correction and shimming using cloverleaf navigators
Magnetic Resonance in Medicine, 2006Co-Authors: André J. W. Van Der Kouwe, Thomas Benner, Anders M DaleAbstract:Subject Motion during scanning can greatly reduce MRI image quality and is a major reason for discarding data in both clinical and research scanning. The quality of the high-resolution structural data used for morphometric analysis is especially compromised by subject movement because high-resolution scans are of longer duration. A method is presented that measures and corrects Rigid Body Motion and associated first-order shim changes in real time, using a pulse sequence with embedded cloverleaf navigators and a feedback control mechanism. The procedure requires a 12-s preliminary mapping scan. A single-path, 4.2-ms cloverleaf navigator is inserted every repetition time (TR) after the readout of a 3D fast low-angle shot (FLASH) sequence, requiring no additional RF pulses and minimally impacting scan duration. Every TR, a Rigid Body Motion estimate is made and a correction is fed back to adjust the gradients and shim offsets. Images are corrected and reconstructed on the scanner computer for immediate access. Correction for between-scan Motion can be accomplished by using the same reference map for each scan repetition. Human and phantom tests demonstrated a consistent improvement in image quality if Motion occurred during the acquisition.
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Real‐time Rigid Body Motion correction and shimming using cloverleaf navigators
Magnetic resonance in medicine, 2006Co-Authors: André J. W. Van Der Kouwe, Thomas Benner, Anders M DaleAbstract:Subject Motion during scanning can greatly reduce MRI image quality and is a major reason for discarding data in both clinical and research scanning. The quality of the high-resolution structural data used for morphometric analysis is especially compromised by subject movement because high-resolution scans are of longer duration. A method is presented that measures and corrects Rigid Body Motion and associated first-order shim changes in real time, using a pulse sequence with embedded cloverleaf navigators and a feedback control mechanism. The procedure requires a 12-s preliminary mapping scan. A single-path, 4.2-ms cloverleaf navigator is inserted every repetition time (TR) after the readout of a 3D fast low-angle shot (FLASH) sequence, requiring no additional RF pulses and minimally impacting scan duration. Every TR, a Rigid Body Motion estimate is made and a correction is fed back to adjust the gradients and shim offsets. Images are corrected and reconstructed on the scanner computer for immediate access. Correction for between-scan Motion can be accomplished by using the same reference map for each scan repetition. Human and phantom tests demonstrated a consistent improvement in image quality if Motion occurred during the acquisition.
Yao Guo - One of the best experts on this subject based on the ideXlab platform.
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RRV: A Spatiotemporal Descriptor for Rigid Body Motion Recognition
IEEE Transactions on Cybernetics, 2017Co-Authors: Yao Guo, Youfu Li, Zhanpeng ShaoAbstract:The Motion behaviors of a Rigid Body can be characterized by a six degrees of freedom Motion trajectory, which contains the 3-D position vectors of a reference point on the Rigid Body and 3-D rotations of this Rigid Body over time. This paper devises a rotation and relative velocity (RRV) descriptor by exploring the local translational and rotational invariants of Rigid Body Motion trajectories, which is insensitive to noise, invariant to Rigid transformation and scale. The RRV descriptor is then applied to characterize Motions of a human Body skeleton modeled as articulated interconnections of multiple Rigid bodies. To show the descriptive ability of our RRV descriptor, we explore its potentials and applications in different Rigid Body Motion recognition tasks. The experimental results on benchmark datasets demonstrate that our RRV descriptor learning discriminative Motion patterns can achieve superior results for various recognition tasks.
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IROS - MSM-HOG: A flexible trajectory descriptor for Rigid Body Motion recognition
2017 IEEE RSJ International Conference on Intelligent Robots and Systems (IROS), 2017Co-Authors: Yao Guo, Zhanpeng ShaoAbstract:This paper proposes a flexible descriptor for representing 6-D Rigid Body Motion trajectories, which not only shows strong invariances and descriptive ability but also achieves satisfactory results in both recognition accuracy and efficiency. 6-D Rigid Body Motion trajectories are first transformed into the Multi-layer Self-similarity Matrices (MSM) representation. The MSM is the combination of the square similarity matrices in three layers, which captures both local and global spatiotemporal features of the trajectories. Next, the Histogram of Oriented Gradients (HOG) features extracted from the MSM representation are concatenated as the final MSM-HOG trajectory descriptor. Then we train the Support Vector Machine (SVM) classifier with the linear kernel for multicalss Motion recognition. Finally, Rigid Body Motion recognition experiments on two public datasets are conducted to verify the effectiveness and efficiency of the proposed method.
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DSRF: A flexible descriptor for effective Rigid Body Motion trajectory recognition
2016 IEEE International Conference on Mechatronics and Automation, 2016Co-Authors: Yao Guo, Zhanpeng ShaoAbstract:Rigid Body Motion trajectories can provide sufficient clues in understanding Motion behaviors of objects of interest. An invariant descriptor for a Motion trajectory can offer substantial advantages over raw data. This paper firstly proposes a Dual Square-Root Function (DSRF) descriptor by only calculating gradient-based shape features of normalized Rigid Body Motion trajectories, while high-order time derivatives are involved in previous works. Our DSRF descriptor has shown richness in description, moreover, it is invariant to scaling, Rigid transformation, robust to noise and beneficial for matching rate-variance trajectories. To illustrate these, we then evaluate DSRF descriptor for different trajectory-based Rigid Body Motion recognition tasks. Experimental results on two benchmark datasets demonstrate that it outperforms previous ones in terms of the recognition accuracy and robustness.
André J. W. Van Der Kouwe - One of the best experts on this subject based on the ideXlab platform.
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Real-time Rigid Body Motion correction and shimming using cloverleaf navigators
Magnetic Resonance in Medicine, 2006Co-Authors: André J. W. Van Der Kouwe, Thomas Benner, Anders M DaleAbstract:Subject Motion during scanning can greatly reduce MRI image quality and is a major reason for discarding data in both clinical and research scanning. The quality of the high-resolution structural data used for morphometric analysis is especially compromised by subject movement because high-resolution scans are of longer duration. A method is presented that measures and corrects Rigid Body Motion and associated first-order shim changes in real time, using a pulse sequence with embedded cloverleaf navigators and a feedback control mechanism. The procedure requires a 12-s preliminary mapping scan. A single-path, 4.2-ms cloverleaf navigator is inserted every repetition time (TR) after the readout of a 3D fast low-angle shot (FLASH) sequence, requiring no additional RF pulses and minimally impacting scan duration. Every TR, a Rigid Body Motion estimate is made and a correction is fed back to adjust the gradients and shim offsets. Images are corrected and reconstructed on the scanner computer for immediate access. Correction for between-scan Motion can be accomplished by using the same reference map for each scan repetition. Human and phantom tests demonstrated a consistent improvement in image quality if Motion occurred during the acquisition.
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Real‐time Rigid Body Motion correction and shimming using cloverleaf navigators
Magnetic resonance in medicine, 2006Co-Authors: André J. W. Van Der Kouwe, Thomas Benner, Anders M DaleAbstract:Subject Motion during scanning can greatly reduce MRI image quality and is a major reason for discarding data in both clinical and research scanning. The quality of the high-resolution structural data used for morphometric analysis is especially compromised by subject movement because high-resolution scans are of longer duration. A method is presented that measures and corrects Rigid Body Motion and associated first-order shim changes in real time, using a pulse sequence with embedded cloverleaf navigators and a feedback control mechanism. The procedure requires a 12-s preliminary mapping scan. A single-path, 4.2-ms cloverleaf navigator is inserted every repetition time (TR) after the readout of a 3D fast low-angle shot (FLASH) sequence, requiring no additional RF pulses and minimally impacting scan duration. Every TR, a Rigid Body Motion estimate is made and a correction is fed back to adjust the gradients and shim offsets. Images are corrected and reconstructed on the scanner computer for immediate access. Correction for between-scan Motion can be accomplished by using the same reference map for each scan repetition. Human and phantom tests demonstrated a consistent improvement in image quality if Motion occurred during the acquisition.
