The Experts below are selected from a list of 174 Experts worldwide ranked by ideXlab platform

Qin Zhu - One of the best experts on this subject based on the ideXlab platform.

  • perceiving the affordance of string tension for power strokes in badminton expertise allows effective use of all string tensions
    Journal of Sports Sciences, 2013
    Co-Authors: Qin Zhu
    Abstract:

    Abstract Affordances mean opportunities for action. These affordances are important for sports performance and relevant to the abilities developed by skilled athletes. In racquet sports such as badminton, different players prefer widely different string tension because it is believed to provide opportunities for effective strokes. The current study examined whether badminton players can perceive the affordance of string tension for power strokes and whether the perception of affordance itself changed as a function of skill level. The results showed that string tension constrained the striking performance of both novice and recreational players, but not of expert players. When Perceptual Capability was assessed, Perceptual mode did not affect perception of the optimal string tension. Skilled players successfully perceived the affordance of string tension, but only experts were concerned about saving energy. Our findings demonstrated that perception of the affordance of string tension in badminton was deter...

Winfried Ilg - One of the best experts on this subject based on the ideXlab platform.

  • Motor expertise facilitates the accuracy of state extrapolation in perception.
    PloS one, 2017
    Co-Authors: Nicolas Ludolph, Jannis Plöger, Martin A. Giese, Winfried Ilg
    Abstract:

    Predicting the behavior of objects in the environment is an important requirement to overcome latencies in the sensorimotor system and realize precise actions in rapid situations. Internal forward models that were acquired during motor training might not only be used for efficiently controlling fast motor behavior but also to facilitate extrapolation performance in purely Perceptual tasks. In this study, we investigated whether preceding virtual cart-pole balancing training facilitates the ability to extrapolate the virtual pole motion. Specifically, subjects had to report the expected pole orientation after an occlusion of the pole of 900ms duration. We compared a group of 10 subjects, proficient in performing the virtual cart-pole balancing task, to 10 naive subjects without motor experience in cart-pole balancing task. Our results demonstrate that preceding motor training increases the accuracy of pole movement extrapolation, although extrapolation is not trained explicitly. Additionally, we modelled subjects’ behaviors and show that the difference in extrapolation performance can be explained by individual differences in the accuracy of internal forward models. When subjects are provided with feedback about the true orientation of the pole after the occlusion in a second phase of the experiment, both groups improve rapidly. The results indicate that the Perceptual Capability to extrapolate the state of the cart-pole system accurately is implicitly trained during motor learning. We discuss these results in the context of shared representations and action-perception transfer.

  • Motor expertise facilitates the precision of state extrapolation in perception
    2017
    Co-Authors: Nicolas Ludolph, Jannis Plöger, Martin A. Giese, Winfried Ilg
    Abstract:

    Predicting the behavior of objects in the environment is an important requirement to overcome latencies in the sensorimotor system and realize precise actions in rapid situations. Internal forward models that were acquired during motor training might not only be used for efficiently controlling fast motor behavior but also to facilitate extrapolation performance in purely Perceptual tasks. In this study, we investigated whether preceding virtual cart-pole balancing training facilitates the ability to extrapolate the pole motion. We compared a group of 10 subjects, proficient in performing the cart-pole balancing task, to 10 naive subjects. Our results demonstrate that preceding motor training increases the precision of pole movement extrapolation, although extrapolation is not trained explicitly. Additionally, we modelled subjects' behaviors and show that the difference in extrapolation performance can be explained by individual differences in the accuracy of internal forward models. When subjects are provided with feedback about the true pole movement in a second phase, both groups improve rapidly. The results indicate that the Perceptual Capability to extrapolate the state of the cart-pole system accurately is implicitly trained during motor learning. We discuss these results in the context of shared representations and action-perception transfer.

Sukhendu Das - One of the best experts on this subject based on the ideXlab platform.

  • AVSS - Generic Object Recognition Using a Combination of ICA and Shape Cues
    2006 IEEE International Conference on Video and Signal Based Surveillance, 2006
    Co-Authors: Manisha Kalra, Sukhendu Das, Amitava Datta
    Abstract:

    This paper addresses the problem of Generic Object Recognition by modeling the Perceptual Capability of human beings. In contrast to the traditional approaches, we have approached the recognition problem by proposing a framework which involves two stages of processing. First, an intelligent generic recognizer based on independent component analysis (ICA) is employed to reduce the search space to a few rank-ordered samples. It is shown that ICA captures the appearance characteristics of objects. Shape cues (distance transform based matching) are then used to verify the result of the appearance-based classifier and identify the correct object class and pose. Experiments were conducted using objects with complex appearance and shape characteristics. Sensitivity of recognition to the number of independent components and number of learning samples is analyzed on COIL-100 database. The performance of the generic classifier using ICA with and without shape matching is also analyzed.

  • Pose invariant generic object recognition with orthogonal axis manifolds in linear subspace
    Lecture Notes in Computer Science, 2006
    Co-Authors: Manisha Kalra, P. Deepti, R. Abhilash, Sukhendu Das
    Abstract:

    This paper addresses the problem of pose invariant Generic Object Recognition by modeling the Perceptual Capability of human beings. We propose a novel framework using a combination of appearance and shape cues to recognize the object class and viewpoint (axis of rotation) as well as determine its pose (angle of view). The appearance model of the object from multiple viewpoints is captured using Linear Subspace Analysis techniques and is used to reduce the search space to a few rank-ordered candidates. We have used a decision-fusion based combination of 2D PCA and ICA to integrate the complementary information of classifiers and improve recognition accuracy. For matching based on shape features, we propose the use of distance transform based correlation. A decision fusion using Sum Rule of 2D PCA and ICA subspace classifiers, and distance transform based correlation is then used to verify the correct object class and determine its viewpoint and pose. Experiments were conducted on COIL-100 and IGOIL (IITM Generic Object Image Library) databases which contain objects with complex appearance and shape characteristics. IGOIL database was captured to analyze the appearance manifolds along two orthogonal axes of rotation.

  • ICVGIP - Pose invariant generic object recognition with orthogonal axis manifolds in linear subspace
    Computer Vision Graphics and Image Processing, 2006
    Co-Authors: Manisha Kalra, P. Deepti, R. Abhilash, Sukhendu Das
    Abstract:

    This paper addresses the problem of pose invariant Generic Object Recognition by modeling the Perceptual Capability of human beings. We propose a novel framework using a combination of appearance and shape cues to recognize the object class and viewpoint (axis of rotation) as well as determine its pose (angle of view). The appearance model of the object from multiple viewpoints is captured using Linear Subspace Analysis techniques and is used to reduce the search space to a few rank-ordered candidates. We have used a decision-fusion based combination of 2D PCA and ICA to integrate the complementary information of classifiers and improve recognition accuracy. For matching based on shape features, we propose the use of distance transform based correlation. A decision fusion using ‘Sum Rule' of 2D PCA and ICA subspace classifiers, and distance transform based correlation is then used to verify the correct object class and determine its viewpoint and pose. Experiments were conducted on COIL-100 and IGOIL (IITM Generic Object Image Library) databases which contain objects with complex appearance and shape characteristics. IGOIL database was captured to analyze the appearance manifolds along two orthogonal axes of rotation.

  • A Framework for Fusion of 3D Appearance and 2D Shape Cues for Generic Object Recognition
    Advances in Pattern Recognition, 2006
    Co-Authors: Manisha Kalra, Sunando Sengupta, Sukhendu Das
    Abstract:

    Abstract This paper addresses the problem of Generic Object Recognition (GOR) from arbitrary viewpointsby modeling the Perceptual Capability of human beings. We propose a novel framework which uses acombination of 3D appearance and 2D shape cues to recognize the object class as well as determineits pose. We propose a hierarchical framework for GOR, which combines two stages of processing.First, the 3D appearance model of the object is captured from multiple viewpoints using LinearSubspace Analysis techniques. These appearance cues are used to reduce the search space to a fewrank-ordered samples. We have used a decision-fusion based combination of 2D PCA and ICA tointegrate the complementary information of classifiers and improve appearance-based recognitionaccuracy. Shape matching is then performed on the reduced search space, using either distancetransform based correlation or shape context based matching. The proposed framework for GORuses a decision fusion technique, in which evidences from 3D appearance and 2D shape are combined(fused) to obtain the correct object class and its pose. Experiments were conducted using objects withcomplex appearance and shape characteristics, and the performance of the proposed framework hasbeen shown to be superior, using the COIL-100 and IGOIL (IITM Generic Object Image Library)databases. IGOIL database was also used to analyze the appearance manifolds along two orthogonalaxis of rotation. Performance degradation in case of noisy images has also been presented.Keywords: Generic Object Recognition, Linear Subspace Analysis, Appearance, Manifolds, Shape,Distance Transform, Shape Context, Classifier Fusion

Frank E. Pollick - One of the best experts on this subject based on the ideXlab platform.

  • Overlapping but Divergent Neural Correlates Underpinning Audiovisual Synchrony and Temporal Order Judgments
    Frontiers in human neuroscience, 2018
    Co-Authors: Scott Love, Karin Petrini, Cyril Pernet, Marianne Latinus, Frank E. Pollick
    Abstract:

    Multisensory processing is a core Perceptual Capability, and the need to understand its neural bases provides a fundamental problem in the study of brain function. Both synchrony and temporal order judgments are commonly used to investigate synchrony perception between different sensory cues and multisensory perception in general. However, extensive behavioral evidence indicates that these tasks do not measure identical Perceptual processes. Here we used functional magnetic resonance imaging to investigate how behavioral differences between the tasks are instantiated as neural differences. As these neural differences could manifest at either the sustained (task/state-related) and/or transient (event-related) levels of processing, a mixed block/event-related design was used to investigate the neural response of both time-scales. Clear differences in both sustained and transient BOLD responses were observed between the two tasks, consistent with behavioral differences indeed arising from overlapping but divergent neural mechanisms. Temporal order judgments, but not synchrony judgments, required transient activation in several left hemisphere regions, which may reflect increased task demands caused by an extra stage of processing. Our results highlight that multisensory integration mechanisms can be task dependent, which, in particular, has implications for the study of atypical temporal processing in clinical populations.

  • Image_1_Overlapping but Divergent Neural Correlates Underpinning Audiovisual Synchrony and Temporal Order Judgments.PDF
    2018
    Co-Authors: Scott A. Love, Karin Petrini, Marianne Latinus, Cyril R. Pernet, Frank E. Pollick
    Abstract:

    Multisensory processing is a core Perceptual Capability, and the need to understand its neural bases provides a fundamental problem in the study of brain function. Both synchrony and temporal order judgments are commonly used to investigate synchrony perception between different sensory cues and multisensory perception in general. However, extensive behavioral evidence indicates that these tasks do not measure identical Perceptual processes. Here we used functional magnetic resonance imaging to investigate how behavioral differences between the tasks are instantiated as neural differences. As these neural differences could manifest at either the sustained (task/state-related) and/or transient (event-related) levels of processing, a mixed block/event-related design was used to investigate the neural response of both time-scales. Clear differences in both sustained and transient BOLD responses were observed between the two tasks, consistent with behavioral differences indeed arising from overlapping but divergent neural mechanisms. Temporal order judgments, but not synchrony judgments, required transient activation in several left hemisphere regions, which may reflect increased task demands caused by an extra stage of processing. Our results highlight that multisensory integration mechanisms can be task dependent, which, in particular, has implications for the study of atypical temporal processing in clinical populations.

Nicolas Ludolph - One of the best experts on this subject based on the ideXlab platform.

  • Motor expertise facilitates the accuracy of state extrapolation in perception.
    PloS one, 2017
    Co-Authors: Nicolas Ludolph, Jannis Plöger, Martin A. Giese, Winfried Ilg
    Abstract:

    Predicting the behavior of objects in the environment is an important requirement to overcome latencies in the sensorimotor system and realize precise actions in rapid situations. Internal forward models that were acquired during motor training might not only be used for efficiently controlling fast motor behavior but also to facilitate extrapolation performance in purely Perceptual tasks. In this study, we investigated whether preceding virtual cart-pole balancing training facilitates the ability to extrapolate the virtual pole motion. Specifically, subjects had to report the expected pole orientation after an occlusion of the pole of 900ms duration. We compared a group of 10 subjects, proficient in performing the virtual cart-pole balancing task, to 10 naive subjects without motor experience in cart-pole balancing task. Our results demonstrate that preceding motor training increases the accuracy of pole movement extrapolation, although extrapolation is not trained explicitly. Additionally, we modelled subjects’ behaviors and show that the difference in extrapolation performance can be explained by individual differences in the accuracy of internal forward models. When subjects are provided with feedback about the true orientation of the pole after the occlusion in a second phase of the experiment, both groups improve rapidly. The results indicate that the Perceptual Capability to extrapolate the state of the cart-pole system accurately is implicitly trained during motor learning. We discuss these results in the context of shared representations and action-perception transfer.

  • Motor expertise facilitates the precision of state extrapolation in perception
    2017
    Co-Authors: Nicolas Ludolph, Jannis Plöger, Martin A. Giese, Winfried Ilg
    Abstract:

    Predicting the behavior of objects in the environment is an important requirement to overcome latencies in the sensorimotor system and realize precise actions in rapid situations. Internal forward models that were acquired during motor training might not only be used for efficiently controlling fast motor behavior but also to facilitate extrapolation performance in purely Perceptual tasks. In this study, we investigated whether preceding virtual cart-pole balancing training facilitates the ability to extrapolate the pole motion. We compared a group of 10 subjects, proficient in performing the cart-pole balancing task, to 10 naive subjects. Our results demonstrate that preceding motor training increases the precision of pole movement extrapolation, although extrapolation is not trained explicitly. Additionally, we modelled subjects' behaviors and show that the difference in extrapolation performance can be explained by individual differences in the accuracy of internal forward models. When subjects are provided with feedback about the true pole movement in a second phase, both groups improve rapidly. The results indicate that the Perceptual Capability to extrapolate the state of the cart-pole system accurately is implicitly trained during motor learning. We discuss these results in the context of shared representations and action-perception transfer.