Visual motion perception for mobile robots through dense optical flow fields

2017 ◽  
Vol 87 ◽  
pp. 1-14 ◽  
Author(s):  
Andry Maykol Pinto ◽  
Paulo G. Costa ◽  
Miguel V. Correia ◽  
Anibal C. Matos ◽  
A. Paulo Moreira
Keyword(s):  
Mobile Robots ◽  
Optical Flow ◽  
Motion Perception ◽  
Visual Motion ◽  
Flow Fields ◽  
Visual Motion Perception ◽  
Dense Optical Flow

Role of the Vermal Cerebellum in Visually Guided Eye Movements and Visual Motion Perception

2019 ◽  
Vol 5 (1) ◽  
pp. 247-268 ◽  
Author(s):  
Peter Thier ◽  
Akshay Markanday
Keyword(s):  
Eye Movements ◽  
Motion Perception ◽  
Visual Motion ◽  
Cerebellar Nuclei ◽  
Visually Guided ◽  
Past Performance ◽  
Visual Motion Perception ◽  
Oculomotor Vermis ◽  
Retinal Information

The cerebellar cortex is a crystal-like structure consisting of an almost endless repetition of a canonical microcircuit that applies the same computational principle to different inputs. The output of this transformation is broadcasted to extracerebellar structures by way of the deep cerebellar nuclei. Visually guided eye movements are accommodated by different parts of the cerebellum. This review primarily discusses the role of the oculomotor part of the vermal cerebellum [the oculomotor vermis (OMV)] in the control of visually guided saccades and smooth-pursuit eye movements. Both types of eye movements require the mapping of retinal information onto motor vectors, a transformation that is optimized by the OMV, considering information on past performance. Unlike the role of the OMV in the guidance of eye movements, the contribution of the adjoining vermal cortex to visual motion perception is nonmotor and involves a cerebellar influence on information processing in the cerebral cortex.

scholarly journals Human visual motion perception shows hallmarks of Bayesian structural inference

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sichao Yang ◽  
Johannes Bill ◽  
Jan Drugowitsch ◽  
Samuel J. Gershman
Keyword(s):  
Motion Perception ◽  
Visual Motion ◽  
Probabilistic Reasoning ◽  
Choice Model ◽  
Human Motion ◽  
Object Motion ◽  
Ideal Observer ◽  
Structure Identification ◽  
Structural Inference ◽  
Visual Motion Perception

AbstractMotion relations in visual scenes carry an abundance of behaviorally relevant information, but little is known about how humans identify the structure underlying a scene’s motion in the first place. We studied the computations governing human motion structure identification in two psychophysics experiments and found that perception of motion relations showed hallmarks of Bayesian structural inference. At the heart of our research lies a tractable task design that enabled us to reveal the signatures of probabilistic reasoning about latent structure. We found that a choice model based on the task’s Bayesian ideal observer accurately matched many facets of human structural inference, including task performance, perceptual error patterns, single-trial responses, participant-specific differences, and subjective decision confidence—especially, when motion scenes were ambiguous and when object motion was hierarchically nested within other moving reference frames. Our work can guide future neuroscience experiments to reveal the neural mechanisms underlying higher-level visual motion perception.

scholarly journals Inversion of Perceived Direction of Motion Caused by Spatial Undersampling in Two Children with Periventricular Leukomalacia

2008 ◽  
Vol 20 (6) ◽  
pp. 1094-1106 ◽  
Author(s):  
Maria Concetta Morrone ◽  
Andrea Guzzetta ◽  
Francesca Tinelli ◽  
Michela Tosetti ◽  
Michela Del Viva ◽  
...  
Keyword(s):  
Motion Perception ◽  
Video Game ◽  
Visual Motion ◽  
Periventricular Leukomalacia ◽  
Translational Motion ◽  
High Sensitivity ◽  
Strong Response ◽  
Visual Motion Perception ◽  
Spatial Frequencies ◽  
Random Dot Patterns

We report here two cases of two young diplegic patients with cystic periventricular leukomalacia who systematically, and with high sensitivity, perceive translational motion of a random-dot display in the opposite direction. The apparent inversion was specific for translation motion: Rotation and expansion motion were perceived correctly, with normal sensitivity. It was also specific for random-dot patterns, not occurring with gratings. For the one patient that we were able to test extensively, contrast sensitivity for static stimuli was normal, but was very low for direction discrimination at high spatial frequencies and all temporal frequencies. His optokinetic nystagmus movements were normal but he was unable to track a single translating target, indicating a perceptual origin of the tracking deficit. The severe deficit for motion perception was also evident in the seminatural situation of a driving simulation video game. The perceptual deficit for translational motion was reinforced by functional magnetic resonance imaging studies. Translational motion elicited no response in the MT complex, although it did produce a strong response in many visual areas when contrasted with blank stimuli. However, radial and rotational motion produced a normal pattern of activation in a subregion of the MT complex. These data reinforce the existent evidence for independent cortical processing for translational, and circular or radial flow motion, and further suggest that the two systems have different vulnerability and plasticity to prenatal damage. They also highlight the complexity of visual motion perception, and how the delicate balance of neural activity can lead to paradoxical effects such as consistent misperception of the direction of motion. We advance a possible explanation of a reduced spatial sampling of the motion stimuli and report a simple model that simulates well the experimental results.

scholarly journals A Hardware-Friendly Optical Flow-Based Time-to-Collision Estimation Algorithm

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 807
Author(s):  
Cong Shi ◽  
Zhuoran Dong ◽  
Shrinivas Pundlik ◽  
Gang Luo
Keyword(s):  
Embedded System ◽  
Optical Flow ◽  
Random Forests ◽  
Visual Motion ◽  
Hardware Acceleration ◽  
Ground Truth ◽  
Estimation Algorithm ◽  
Estimation Accuracy ◽  
Time To Collision ◽  
Dense Optical Flow

This work proposes a hardware-friendly, dense optical flow-based Time-to-Collision (TTC) estimation algorithm intended to be deployed on smart video sensors for collision avoidance. The algorithm optimized for hardware first extracts biological visual motion features (motion energies), and then utilizes a Random Forests regressor to predict robust and dense optical flow. Finally, TTC is reliably estimated from the divergence of the optical flow field. This algorithm involves only feed-forward data flows with simple pixel-level operations, and hence has inherent parallelism for hardware acceleration. The algorithm offers good scalability, allowing for flexible tradeoffs among estimation accuracy, processing speed and hardware resource. Experimental evaluation shows that the accuracy of the optical flow estimation is improved due to the use of Random Forests compared to existing voting-based approaches. Furthermore, results show that estimated TTC values by the algorithm closely follow the ground truth. The specifics of the hardware design to implement the algorithm on a real-time embedded system are laid out.

scholarly journals Influence of Active Manipulation of an Object on Visual Motion Perception

i-Perception ◽  
10.1068/ic884 ◽  
2011 ◽  
Vol 2 (8) ◽  
pp. 884-884
Author(s):  
Kazumichi Matsumiya ◽  
Satoshi Shioiri
Keyword(s):  
Motion Perception ◽  
Visual Motion ◽  
Visual Motion Perception
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