Progress meeting ANR TRAJECTORY

Time
January 15th, 2018
Location
INT
General presentation of the grant

Anr TRAJECTORY

Overview of my current projects

http://blog.invibe.net/files/2017-11-15_ColloqueMaster.html

MotionClouds with trajectories

http://motionclouds.invibe.net/posts/2018-01-16-testing-more-complex-trajectories.html

Presentations/2012-04-16_InriaIntMeeting/sequence_DCBA.gif

Presentations/2012-04-16_InriaIntMeeting/sequence_ABCD.gif

A predictive sequence is essential in resolving the coherence problem. The sequence in which a set of local motion is shown is essential for the detection of global motion. we replicate here the experiments by Scott Watamaniuk and colleagues. They have shown behaviourally that a dot in noise is much more detectable when it follows a coherent trajectory, up to an order of magnitude of 10 times what would be predicted by the local components of the trajectory. (Left) In this first movie we observe white noise and at first sight, no information is detectable. In fact, there is a dot moving along some smooth linear trajectory, but we broke this trajectory into eight equal parts and shuffled their order in the movie. (Right) if we re-arrange these local motions to be compatible with a predictive sequence, it is much easier to see the dot (from left to right in the top of the image, a smooth pursuit helps to catch it). This simple experiment shows that, even if local motion is similar in both movies, a coherent trajectory is more easy to track. Obviously, we may thus conclude that the whole trajectory is more that its individual parts, and that the independence hypothesis does not hold if we want to account for the predictive information in input sequences such as seems to be crucial for the AP.


This work was supported by ANR project "TRAJECTORY" N° ANR-15-CE37-0011.
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