The Experts below are selected from a list of 8622 Experts worldwide ranked by ideXlab platform

Fumi Kubo - One of the best experts on this subject based on the ideXlab platform.

  • neuronal architecture of a visual center that processes Optic Flow
    Neuron, 2019
    Co-Authors: Anna Kramer, Yunmin Wu, Herwig Baier, Fumi Kubo
    Abstract:

    Summary Animals use global image motion cues to actively stabilize their position by compensatory movements. Neurons in the zebrafish pretectum distinguish different Optic Flow patterns, e.g., rotation and translation, to drive appropriate behaviors. Combining functional imaging and morphological reconstruction of single cells, we revealed critical neuroanatomical features of this sensorimotor transformation. Terminals of direction-selective retinal ganglion cells (DS-RGCs) are located within the pretectal retinal arborization field 5 (AF5), where they meet dendrites of pretectal neurons with simple tuning to monocular Optic Flow. Translation-selective neurons, which respond selectively to Optic Flow in the same direction for both eyes, are intermingled with these simple cells but do not receive inputs from DS-RGCs. Mutually exclusive populations of pretectal projection neurons innervate either the reticular formation or the cerebellum, which in turn control motor responses. We posit that local computations in a defined pretectal circuit transform Optic Flow signals into neural commands driving optomotor behavior. Video Abstract Download : Download video (48MB)

Y. Aloimonos - One of the best experts on this subject based on the ideXlab platform.

  • Statistical biases in Optic Flow
    Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149), 1999
    Co-Authors: C. Fermuller, R. Pless, Y. Aloimonos
    Abstract:

    The computation of Optical Flow from image derivatives is biased in regions of non uniform gradient distributions. A least-squares or total least squares approach to computing Optic Flow from image derivatives even in regions of consistent Flow can lead to a systematic bias dependent upon the direction of the Optic Flow, the distribution of the gradient directions, and the distribution of the image noise. The bias a consistent underestimation of length and a directional error. Similar results hold for various methods of computing Optical Flow in the spatiotemporal frequency domain. The predicted bias in the Optical Flow is consistent with psychophysical evidence of human judgment of the velocity of moving plaids, and provides an explanation of the Ouchi illusion. Correction of the bias requires accurate estimates of the noise distribution; the failure of the human visual system to make these corrections illustrates both the difficulty of the task and the feasibility of using this distorted Optic Flow or undistorted normal Flow in tasks requiring higher lever processing.

  • CVPR - Statistical biases in Optic Flow
    Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149), 1999
    Co-Authors: C. Fermuller, R. Pless, Y. Aloimonos
    Abstract:

    The computation of Optical Flow from image derivatives is biased in regions of non uniform gradient distributions. A least-squares or total least squares approach to computing Optic Flow from image derivatives even in regions of consistent Flow can lead to a systematic bias dependent upon the direction of the Optic Flow, the distribution of the gradient directions, and the distribution of the image noise. The bias a consistent underestimation of length and a directional error. Similar results hold for various methods of computing Optical Flow in the spatiotemporal frequency domain. The predicted bias in the Optical Flow is consistent with psychophysical evidence of human judgment of the velocity of moving plaids, and provides an explanation of the Ouchi illusion. Correction of the bias requires accurate estimates of the noise distribution; the failure of the human visual system to make these corrections illustrates both the difficulty of the task and the feasibility of using this distorted Optic Flow or undistorted normal Flow in tasks requiring higher lever processing.

David Suter - One of the best experts on this subject based on the ideXlab platform.

  • robust Optic Flow computation
    International Journal of Computer Vision, 1998
    Co-Authors: Alireza Babhadiashar, David Suter
    Abstract:

    This paper formulates the Optic Flow problem as a set of over-determined simultaneous linear equations. It then introduces and studies two new robust Optic Flow methods. The first technique is based on using the Least Median of Squares (LMedS) to detect the outliers. Then, the inlier group is solved using the least square technique. The second method employs a new robust statistical method named the Least Median of Squares Orthogonal Distances (LMSOD) to identify the outliers and then uses total least squares to solve the Optic Flow problem. The performance of both methods are studied by experiments on synthetic and real image sequences. These methods outperform other published methods both in accuracy and robustness.

C. Fermuller - One of the best experts on this subject based on the ideXlab platform.

  • Statistical biases in Optic Flow
    Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149), 1999
    Co-Authors: C. Fermuller, R. Pless, Y. Aloimonos
    Abstract:

    The computation of Optical Flow from image derivatives is biased in regions of non uniform gradient distributions. A least-squares or total least squares approach to computing Optic Flow from image derivatives even in regions of consistent Flow can lead to a systematic bias dependent upon the direction of the Optic Flow, the distribution of the gradient directions, and the distribution of the image noise. The bias a consistent underestimation of length and a directional error. Similar results hold for various methods of computing Optical Flow in the spatiotemporal frequency domain. The predicted bias in the Optical Flow is consistent with psychophysical evidence of human judgment of the velocity of moving plaids, and provides an explanation of the Ouchi illusion. Correction of the bias requires accurate estimates of the noise distribution; the failure of the human visual system to make these corrections illustrates both the difficulty of the task and the feasibility of using this distorted Optic Flow or undistorted normal Flow in tasks requiring higher lever processing.

  • CVPR - Statistical biases in Optic Flow
    Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149), 1999
    Co-Authors: C. Fermuller, R. Pless, Y. Aloimonos
    Abstract:

    The computation of Optical Flow from image derivatives is biased in regions of non uniform gradient distributions. A least-squares or total least squares approach to computing Optic Flow from image derivatives even in regions of consistent Flow can lead to a systematic bias dependent upon the direction of the Optic Flow, the distribution of the gradient directions, and the distribution of the image noise. The bias a consistent underestimation of length and a directional error. Similar results hold for various methods of computing Optical Flow in the spatiotemporal frequency domain. The predicted bias in the Optical Flow is consistent with psychophysical evidence of human judgment of the velocity of moving plaids, and provides an explanation of the Ouchi illusion. Correction of the bias requires accurate estimates of the noise distribution; the failure of the human visual system to make these corrections illustrates both the difficulty of the task and the feasibility of using this distorted Optic Flow or undistorted normal Flow in tasks requiring higher lever processing.

M.a. Lewis - One of the best experts on this subject based on the ideXlab platform.

  • ICRA - Detecting surface features during locomotion using Optic Flow
    Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292), 2002
    Co-Authors: M.a. Lewis
    Abstract:

    We test the hypothesis that: (1) Optic Flow can be used to detect significant environmental features during locomotion in a biped, even given significant up and down movement and jarring of the robot during locomotion. (2) Reliable detection is only possible if a prediction of the expected Optic Flow field is made at each instance. This prediction should be driven by the phase of the robot's gait as well as other information about the state of the robot. (3) This prediction can be accomplished in a distributed, biologically plausible framework. Our results using a walking biped mechanism strongly support this hypothesis and indicate that Optic Flow is a viable strategy and that the prediction of Optic Flow is a critical component in this behavior.

  • Detecting surface features during locomotion using Optic Flow
    Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292), 2002
    Co-Authors: M.a. Lewis
    Abstract:

    We test the hypothesis that: (1) Optic Flow can be used to detect significant environmental features during locomotion in a biped, even given significant up and down movement and jarring of the robot during locomotion. (2) Reliable detection is only possible if a prediction of the expected Optic Flow field is made at each instance. This prediction should be driven by the phase of the robot's gait as well as other information about the state of the robot. (3) This prediction can be accomplished in a distributed, biologically plausible framework. Our results using a walking biped mechanism strongly support this hypothesis and indicate that Optic Flow is a viable strategy and that the prediction of Optic Flow is a critical component in this behavior.