Abstract

Perceptual grouping played a prominent role in support of early object
recognition systems, which typically took an input image and a database of
shape models and identified which of the models was visible in the image. When
the database was large, local features were not sufficiently distinctive to
prune down the space of models to a manageable number that could be verified.
 However, when causally related shape features were grouped, using
intermediate-level shape priors, e.g., co-termination, symmetry, and
compactness, they formed effective shape indices and allowed databases to grow
in size.  In recent years, the recognition (categorization) community has
focused on the object detection problem, in which the input image is searched
for a specific target object.  Since indexing is not required to select the
target model, perceptual grouping is not required to construct a
discriminative shape index; the existence of a much stronger object-level
shape prior precludes the need for a weaker intermediate-level shape prior. As
a result, perceptual grouping activity at our major conferences has
diminished. However, there are clear signs that the recognition community is
moving from appearance back to shape, and from detection back to unexpected
object recognition. Shape-based perceptual grouping will play a critical role
in facilitating this transition.  But while causally related features must be
grouped, they also need to be abstracted before they can be matched to
categorical models.   In this talk, I will describe our recent progress on the
use of intermediate shape priors in segmenting, grouping, and abstracting
shape features. Specifically, I will describe the use of symmetry and
non-accidental attachment to detect and group symmetric parts, and the use of
closure to separate figure from background.