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Vivian Paulun, Roland Fleming; Visual perception of elastic behavior of bouncing objects. Journal of Vision 2017;17(10):225. doi: 10.1167/17.10.225.
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When an object bounces around a scene, its behavior depends on both its intrinsic material properties (e.g., elasticity) and extrinsic factors (e.g., initial position, velocity). Visually inferring elasticity requires disentangling these different contributions to the observed motion. Moreover, although the space of possible trajectories is very large, some motions appear intuitively more plausible than others. Here we investigated how the visual system estimates elasticity and the typicality of object motion from short (2s) simulations in which a cubic object bounced in a room. We varied elasticity in ten even steps and randomly varied the object's start position, orientation and velocity to gain three random samples for each elasticity. Based on these 30 variations we created two reduced versions of each stimulus, showing the cube in a completely black environment (as opposed to a fully rendered room). In one condition the cube was identical to the original stimulus; in the other, the cube rigidly followed the same path without rotating or deforming. Thirteen observers rated the apparent elasticity of the cubes and the typicality of their motion. We found that observers were good at estimating elasticity in all three conditions, i.e. irrespective of whether the scene provided all possible cues or was reduced to the movement path. Some of the random variations produced more typical representatives of a given elasticity than others. Rigid motion was generally perceived as less typical than full motion. The pattern of ratings is consistent with simple heuristics based on the duration and the speed of the motion: The longer and faster an object moved, the higher was its perceived elasticity. The same measures showed a similar but weaker relation to the true elasticity of the cubes. Analysis of the distribution of many trajectories suggests such heuristics can be inferred through unsupervised learning.
Meeting abstract presented at VSS 2017
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