TY - GEN
T1 - A biologically-inspired theory for non-axiomatic parametric curve completion
AU - Ben-Yosef, Guy
AU - Ben-Shahar, Ohad
PY - 2011/3/16
Y1 - 2011/3/16
N2 - Visual curve completion is typically handled in an axiomatic fashion where the shape of the sought-after completed curve follows formal descriptions of desired, image-based perceptual properties (e.g, minimum curvature, roundedness, etc...). Unfortunately, however, these desired properties are still a matter of debate in the perceptual literature. Instead of the image plane, here we study the problem in the mathematical space × that abstracts the cortical areas where curve completion occurs. In this space one can apply basic principles from which perceptual properties in the image plane are derived rather than imposed. In particular, we show how a "least action" principle in × entails many perceptual properties which have support in the perceptual curve completion literature. We formalize this principle in a variational framework for general parametric curves, we derive its differential properties, we present numerical solutions, and we show results on a variety of images.
AB - Visual curve completion is typically handled in an axiomatic fashion where the shape of the sought-after completed curve follows formal descriptions of desired, image-based perceptual properties (e.g, minimum curvature, roundedness, etc...). Unfortunately, however, these desired properties are still a matter of debate in the perceptual literature. Instead of the image plane, here we study the problem in the mathematical space × that abstracts the cortical areas where curve completion occurs. In this space one can apply basic principles from which perceptual properties in the image plane are derived rather than imposed. In particular, we show how a "least action" principle in × entails many perceptual properties which have support in the perceptual curve completion literature. We formalize this principle in a variational framework for general parametric curves, we derive its differential properties, we present numerical solutions, and we show results on a variety of images.
UR - https://www.scopus.com/pages/publications/79952510154
U2 - 10.1007/978-3-642-19309-5_27
DO - 10.1007/978-3-642-19309-5_27
M3 - Conference contribution
AN - SCOPUS:79952510154
SN - 9783642193088
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 346
EP - 359
BT - Computer Vision, ACCV 2010 - 10th Asian Conference on Computer Vision, Revised Selected Papers
T2 - 10th Asian Conference on Computer Vision, ACCV 2010
Y2 - 8 November 2010 through 12 November 2010
ER -