Exploring Coherent Motion Patterns via Structured Trajectory Learning for Crowd Mood Modeling

2017 ◽  
Vol 27 (3) ◽  
pp. 635-648 ◽  
Author(s):  
Yanhao Zhang ◽  
Lei Qin ◽  
Rongrong Ji ◽  
Sicheng Zhao ◽  
Qingming Huang ◽  
...  
Keyword(s):  
Coherent Motion ◽  
Motion Patterns ◽  
Trajectory Learning

Coherent Compound Motion: Corners and Nonrigid Configurations

1990 ◽  
Vol 2 (1) ◽  
pp. 44-57 ◽  
Author(s):  
Steven W. Zucker ◽  
Lee Iverson ◽  
Robert A. Hummel
Keyword(s):  
Neural Computation ◽  
Coherent Motion ◽  
Natural Scenes ◽  
Motion Patterns ◽  
Motion System ◽  
Sinusoidal Gratings ◽  
Single Compound ◽  
Sharp Corners ◽  
Wire Gratings ◽  
Planar Motions

Consider two wire gratings, superimposed and moving across each other. Under certain conditions the two gratings will cohere into a single, compound pattern, which will appear to be moving in another direction. Such coherent motion patterns have been studied for sinusoidal component gratings, and give rise to percepts of rigid, planar motions. In this paper we show how to construct coherent motion displays that give rise to nonuniform, nonrigid, and nonplanar percepts. Most significantly, they also can define percepts with corners. Since these patterns are more consistent with the structure of natural scenes than rigid sinusoidal gratings, they stand as interesting stimuli for both computational and physiological studies. To illustrate, our display with sharp corners (tangent discontinuities or singularities) separating regions of coherent motion suggests that smoothing does not cross tangent discontinuities, a point that argues against existing (regularization) algorithms for computing motion. This leads us to consider how singularities can be confronted directly within optical flow computations, and we conclude with two hypotheses: (1) that singularities are represented within the motion system as multiple directions at the same retinotopic location; and (2) for component gratings to cohere, they must be at the same depth from the viewer. Both hypotheses have implications for the neural computation of coherent motion.

scholarly journals Implied Motion From Form in the Human Visual Cortex

2005 ◽  
Vol 94 (6) ◽  
pp. 4373-4386 ◽  
Author(s):  
Bart Krekelberg ◽  
Argiro Vatakis ◽  
Zoe Kourtzi
Keyword(s):  
Visual Cortex ◽  
Selective Adaptation ◽  
Human Motion ◽  
Coherent Motion ◽  
Local Orientation ◽  
Real Motion ◽  
Motion Patterns ◽  
Human Visual Cortex ◽  
Implied Motion ◽  
Glass Patterns

When cartoonists use speed lines—also called motion streaks—to suggest the speed of a stationary object, they use form to imply motion. The goal of this study was to investigate the mechanisms that mediate the percept of implied motion in the human visual cortex. In an adaptation functional imaging paradigm we presented Glass patterns that, just like speed lines, imply motion but do not on average contain coherent motion energy. We found selective adaptation to these patterns in the human motion complex, the lateral occipital complex (LOC), and earlier visual areas. Glass patterns contain both local orientation features and global structure. To disentangle these aspects we performed a control experiment using Glass patterns with minimal local orientation differences but large global structure differences. This experiment showed that selectivity for Glass patterns arises in part in areas beyond V1 and V2. Interestingly, the selective adaptation transferred from implied motion stimuli to similar real motion patterns in dorsal but not ventral areas. This suggests that the same subpopulations of cells in dorsal areas that are selective for implied motion are also selective for real motion. In other words, these cells are invariant with respect to the cue (implied or real) that generates the motion. We conclude that the human motion complex responds to Glass patterns as if they contain coherent motion. This, presumably, is the reason why these patterns appear to move coherently. The LOC, however, has different cells that respond to the structure of real motion patterns versus implied motion patterns. Such a differential response may allow ventral areas to further analyze the structure of global patterns.

Coherent Motion Discrimination: Impact of Contrast, Age, and Alzheimer's Disease

2000 ◽  
Author(s):  
Grover C. Gilmore ◽  
Sarah R. Morrison ◽  
Lisa D. Townsend ◽  
Cecil W. Thomas
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Coherent Motion ◽  
Motion Discrimination

Tongue and Lip Motion Patterns in Alaryngeal Speech

2020 ◽  
Author(s):  
Kristin J. Teplansky ◽  
Alan Wisler ◽  
Beiming Cao ◽  
Wendy Liang ◽  
Chad W. Whited ◽  
...  
Keyword(s):  
Motion Patterns ◽  
Alaryngeal Speech

Improving motion detection via anodal transcranial direct current stimulation

2020 ◽  
Vol 38 (5) ◽  
pp. 395-405
Author(s):  
Luca Battaglini ◽  
Federica Mena ◽  
Clara Casco
Keyword(s):  
Direct Current ◽  
Signal To Noise Ratio ◽  
Individual Performance ◽  
Coherent Motion ◽  
Motion Processing ◽  
Global Motion ◽  
Local Motion ◽  
Processing Efficiency ◽  
Temporal Area ◽  
Direct Current Stimulation

Background: To study motion perception, a stimulus consisting of a field of small, moving dots is often used. Generally, some of the dots coherently move in the same direction (signal) while the rest move randomly (noise). A percept of global coherent motion (CM) results when many different local motion signals are combined. CM computation is a complex process that requires the integrity of the middle-temporal area (MT/V5) and there is evidence that increasing the number of dots presented in the stimulus makes such computation more efficient. Objective: In this study, we explored whether anodal direct current stimulation (tDCS) over MT/V5 would increase individual performance in a CM task at a low signal-to-noise ratio (SNR, i.e. low percentage of coherent dots) and with a target consisting of a large number of moving dots (high dot numerosity, e.g. >250 dots) with respect to low dot numerosity (<60 dots), indicating that tDCS favour the integration of local motion signal into a single global percept (global motion). Method: Participants were asked to perform a CM detection task (two-interval forced-choice, 2IFC) while they received anodal, cathodal, or sham stimulation on three different days. Results: Our findings showed no effect of cathodal tDCS with respect to the sham condition. Instead, anodal tDCS improves performance, but mostly when dot numerosity is high (>400 dots) to promote efficient global motion processing. Conclusions: The present study suggests that tDCS may be used under appropriate stimulus conditions (low SNR and high dot numerosity) to boost the global motion processing efficiency, and may be useful to empower clinical protocols to treat visual deficits.

scholarly journals Using Different Combinations of Body-Mounted IMU Sensors to Estimate Speed of Horses—A Machine Learning Approach

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 798
Author(s):  
Hamed Darbandi ◽  
Filipe Serra Bragança ◽  
Berend Jan van der Zwaag ◽  
John Voskamp ◽  
Annik Imogen Gmel ◽  
...  
Keyword(s):  
Machine Learning ◽  
The Body ◽  
Biomechanical Analysis ◽  
Estimation Accuracy ◽  
Speed Estimation ◽  
Motion Patterns ◽  
Positioning Systems ◽  
Machine Learning Approach ◽  
Global Positioning ◽  
Measurement Units

Speed is an essential parameter in biomechanical analysis and general locomotion research. It is possible to estimate the speed using global positioning systems (GPS) or inertial measurement units (IMUs). However, GPS requires a consistent signal connection to satellites, and errors accumulate during IMU signals integration. In an attempt to overcome these issues, we have investigated the possibility of estimating the horse speed by developing machine learning (ML) models using the signals from seven body-mounted IMUs. Since motion patterns extracted from IMU signals are different between breeds and gaits, we trained the models based on data from 40 Icelandic and Franches-Montagnes horses during walk, trot, tölt, pace, and canter. In addition, we studied the estimation accuracy between IMU locations on the body (sacrum, withers, head, and limbs). The models were evaluated per gait and were compared between ML algorithms and IMU location. The model yielded the highest estimation accuracy of speed (RMSE = 0.25 m/s) within equine and most of human speed estimation literature. In conclusion, highly accurate horse speed estimation models, independent of IMU(s) location on-body and gait, were developed using ML.

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