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Steven Dakin, Rosilari Bellacosa Marotti; Visual coding of natural contours leads to poor discrimination of object-shape around canonical views. Journal of Vision 2015;15(12):1127. doi: 10.1167/15.12.1127.
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© 2017 Association for Research in Vision and Ophthalmology.
Although progress has been made in understanding how we detect visual contours we know much less about how we encode their shape. Here we describe a psychophysical paradigm that does this by quantifying the perceptual similarity of complex contours: observers decided which of three outline contours (strings of Gabors derived from silhouettes of natural objects) was the “odd-man-out” (where one was derived from a subtly different 3D view of the same object). We estimated the minimum perceptible contour change (i.e. rotation-in-depth) for different starting views of a 3D hand-object. We report poorest discrimination of contours around canonical views (“characteristic” or “typical” object views); small rotations in non-canonical (“unusual”) views tend to have more readily perceptible consequences leading to better performance. This finding extends to other objects, and is robust to random-scaling of contours, to randomization of local orientation structure, and even to replacement of oriented elements with non-oriented Gaussian blobs. We compared our results to predictions from a simple model of shape similarity using cross-correlation of silhouette-images.
Meeting abstract presented at VSS 2015
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