The Experts below are selected from a list of 225 Experts worldwide ranked by ideXlab platform
M. Yachida - One of the best experts on this subject based on the ideXlab platform.
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Iconic Memory-based omnidirectional route panorama navigation
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005Co-Authors: Y. Yagi, K. Tsuji, K. Imai, M. YachidaAbstract:A route navigation method for a mobile robot with an omnidirectional image sensor is described. The route is memorized from a series of consecutive omnidirectional images of the horizon when the robot moves to its goal. While the robot is navigating to the goal point, input is matched against the memorized spatio-temporal route pattern by using dual active contour models and the exact robot position and orientation is estimated from the converged shape of the active contour models.
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Evaluation of Iconic Memory-based ORP navigation
IEEE International Conference on Robotics and Automation 2004. Proceedings. ICRA '04. 2004, 2004Co-Authors: Y. Yagi, K. Tsuji, M. YachidaAbstract:A route navigation method for a mobile robot with an omni-directional image sensor is described. The route is memorized from a series of consecutive omni-directional images at the horizon when the robot moves to the goal. While the robot is navigating to the goal point, the input is matched against the memorized spatio-temporal route pattern by using dual active contour models and the exact robot position and orientation is estimated from the converged shape of active contour models. In this paper, we evaluated the precision of the localization of the robot under several different moving conditions and environments.
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Iconic Memory-based omnidirectional route panorama navigation
2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422), 2003Co-Authors: Y. Yagi, K. Imai, M. YachidaAbstract:A route navigation method for a mobile robot with an omni-directional image sensor is described. The route is memorized from a series of consecutive omnidirectional images at the horizon when the robot moves to the goal. While the robot is navigating to the goal point, the input is matched against the memorized spatio-temporal route pattern by using dual active contour models and the exact robot position and orientation is estimated from the converged shape of active contour models.
Y. Yagi - One of the best experts on this subject based on the ideXlab platform.
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Iconic Memory-based omnidirectional route panorama navigation
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005Co-Authors: Y. Yagi, K. Tsuji, K. Imai, M. YachidaAbstract:A route navigation method for a mobile robot with an omnidirectional image sensor is described. The route is memorized from a series of consecutive omnidirectional images of the horizon when the robot moves to its goal. While the robot is navigating to the goal point, input is matched against the memorized spatio-temporal route pattern by using dual active contour models and the exact robot position and orientation is estimated from the converged shape of the active contour models.
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Evaluation of Iconic Memory-based ORP navigation
IEEE International Conference on Robotics and Automation 2004. Proceedings. ICRA '04. 2004, 2004Co-Authors: Y. Yagi, K. Tsuji, M. YachidaAbstract:A route navigation method for a mobile robot with an omni-directional image sensor is described. The route is memorized from a series of consecutive omni-directional images at the horizon when the robot moves to the goal. While the robot is navigating to the goal point, the input is matched against the memorized spatio-temporal route pattern by using dual active contour models and the exact robot position and orientation is estimated from the converged shape of active contour models. In this paper, we evaluated the precision of the localization of the robot under several different moving conditions and environments.
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Iconic Memory-based omnidirectional route panorama navigation
2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422), 2003Co-Authors: Y. Yagi, K. Imai, M. YachidaAbstract:A route navigation method for a mobile robot with an omni-directional image sensor is described. The route is memorized from a series of consecutive omnidirectional images at the horizon when the robot moves to the goal. While the robot is navigating to the goal point, the input is matched against the memorized spatio-temporal route pattern by using dual active contour models and the exact robot position and orientation is estimated from the converged shape of active contour models.
K. Imai - One of the best experts on this subject based on the ideXlab platform.
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Iconic Memory-based omnidirectional route panorama navigation
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005Co-Authors: Y. Yagi, K. Tsuji, K. Imai, M. YachidaAbstract:A route navigation method for a mobile robot with an omnidirectional image sensor is described. The route is memorized from a series of consecutive omnidirectional images of the horizon when the robot moves to its goal. While the robot is navigating to the goal point, input is matched against the memorized spatio-temporal route pattern by using dual active contour models and the exact robot position and orientation is estimated from the converged shape of the active contour models.
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Iconic Memory-based omnidirectional route panorama navigation
2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422), 2003Co-Authors: Y. Yagi, K. Imai, M. YachidaAbstract:A route navigation method for a mobile robot with an omni-directional image sensor is described. The route is memorized from a series of consecutive omnidirectional images at the horizon when the robot moves to the goal. While the robot is navigating to the goal point, the input is matched against the memorized spatio-temporal route pattern by using dual active contour models and the exact robot position and orientation is estimated from the converged shape of active contour models.
Arien Mack - One of the best experts on this subject based on the ideXlab platform.
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no Iconic Memory without attention
Consciousness and Cognition, 2016Co-Authors: Arien Mack, Muge Erol, Jason Clarke, John BertAbstract:Abstract The experiments reported extend the findings of our earlier paper, (Mack, Erol, & Clarke, 2015) and allow us to reject Bachmann and Aru’s critique of our conclusion (2015) that IM requires attention. They suggested our manipulations, which diverted attention from a letter reporting task in a dual task procedure where the task-cue occurred after the array disappeared, might only have affected access to IM and not the “existence of the phenomenal experience”. By further decreasing the probability of reporting letters to only 10% and adding a final trial in which the letter matrix was either completely absent or distorted, we found more than half our subjects were unaware of its absence, or distortion i.e., were inattentionally blind. We take this as powerful evidence against the existence of any phenomenal experience component of Iconic Memory and consistent with the view that Iconic Memory demands attention and that conscious perception does as well.
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Iconic Memory for natural scenes evidence using a modified change detection procedure
Visual Cognition, 2015Co-Authors: Jason Clarke, Arien MackAbstract:ABSTRACTChange blindness for the contents of natural scenes suggests that only items that are attended while the scene is still visible are stored, leading some to characterize our visual experiences as sparse. Experiments on Iconic Memory for arrays of discrete symbols or objects, however, indicate observers have access to more visual information for at least several hundred milliseconds at offset of a display. In the experiment presented here, we demonstrate an Iconic Memory for complex natural or real-world scenes. Using a modified change detection task in which to-be changed objects are cued at offset of the scene, we show that more information from a natural scene is briefly stored than change blindness predicts and more than is contained in visual short-term Memory. In our experiment, a cue appearing 0, 300, or 1000 msec after offset of the pre-change scene or at onset of the second scene presentation (a Post Cue) directed attention to the location of a possible change. Compared to a no-cue conditio...
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Reply to Bachmann and Aru
Consciousness and Cognition, 2015Co-Authors: Arien Mack, Jason Clarke, Muge ErolAbstract:Abstract A reply to the Bachmann and Aru (2015) critique of our paper (Mack, Erol, & Clarke, 2015) in which we rebut their criticisms and argue once again that our results support our view that Iconic Memory requires attention.
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Iconic Memory is not a case of attention free awareness
Consciousness and Cognition, 2015Co-Authors: Arien Mack, Muge Erol, Jason ClarkeAbstract:Abstract Whether or not awareness entails attention is a much debated question. Since Iconic Memory has been generally assumed to be attention-free, it has been considered an important piece of evidence that it does not (Koch & Tsuchiya, 2007). Therefore the question of the role of attention in Iconic Memory matters. Recent evidence (Persuh, Genzer, & Melara, 2012), suggests that Iconic Memory does depend on attention. Because of the centrality of Iconic Memory to this debate, we looked again at the role of attention in Iconic Memory using a standard whole versus partial report task of letters in a 3 × 2 matrix. We manipulated attention to the array by coupling it with a second task that was either easy or hard and by manipulating the probability of which task was to be performed on any given trial. When attention was maximally diverted from the matrix, participants were able to report less than a single item, confirming the prior results and supporting the conclusion that Iconic Memory entails attention. It is not an instance of attention-free awareness.
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Iconic Memory for the gist of natural scenes
Consciousness and Cognition, 2014Co-Authors: Jason Clarke, Arien MackAbstract:Abstract Does Iconic Memory contain the gist of multiple scenes? Three experiments were conducted. In the first, four scenes from different basic-level categories were briefly presented in one of two conditions: a cue or a no-cue condition. The cue condition was designed to provide an index of the contents of Iconic Memory of the display. Subjects were more sensitive to scene gist in the cue condition than in the no-cue condition. In the second, the scenes came from the same basic-level category. We found no difference in sensitivity between the two conditions. In the third, six scenes from different basic level categories were presented in the visual periphery. Subjects were more sensitive to scene gist in the cue condition. These results suggest that scene gist is contained in Iconic Memory even in the visual periphery; however, Iconic representations are not sufficiently detailed to distinguish between scenes coming from the same category.
K. Tsuji - One of the best experts on this subject based on the ideXlab platform.
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Iconic Memory-based omnidirectional route panorama navigation
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005Co-Authors: Y. Yagi, K. Tsuji, K. Imai, M. YachidaAbstract:A route navigation method for a mobile robot with an omnidirectional image sensor is described. The route is memorized from a series of consecutive omnidirectional images of the horizon when the robot moves to its goal. While the robot is navigating to the goal point, input is matched against the memorized spatio-temporal route pattern by using dual active contour models and the exact robot position and orientation is estimated from the converged shape of the active contour models.
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Evaluation of Iconic Memory-based ORP navigation
IEEE International Conference on Robotics and Automation 2004. Proceedings. ICRA '04. 2004, 2004Co-Authors: Y. Yagi, K. Tsuji, M. YachidaAbstract:A route navigation method for a mobile robot with an omni-directional image sensor is described. The route is memorized from a series of consecutive omni-directional images at the horizon when the robot moves to the goal. While the robot is navigating to the goal point, the input is matched against the memorized spatio-temporal route pattern by using dual active contour models and the exact robot position and orientation is estimated from the converged shape of active contour models. In this paper, we evaluated the precision of the localization of the robot under several different moving conditions and environments.
