1,631 research outputs found
Neural Expectation Maximization
Many real world tasks such as reasoning and physical interaction require
identification and manipulation of conceptual entities. A first step towards
solving these tasks is the automated discovery of distributed symbol-like
representations. In this paper, we explicitly formalize this problem as
inference in a spatial mixture model where each component is parametrized by a
neural network. Based on the Expectation Maximization framework we then derive
a differentiable clustering method that simultaneously learns how to group and
represent individual entities. We evaluate our method on the (sequential)
perceptual grouping task and find that it is able to accurately recover the
constituent objects. We demonstrate that the learned representations are useful
for next-step prediction.Comment: Accepted to NIPS 201
Local and global gestalt laws: A neurally based spectral approach
A mathematical model of figure-ground articulation is presented, taking into
account both local and global gestalt laws. The model is compatible with the
functional architecture of the primary visual cortex (V1). Particularly the
local gestalt law of good continuity is described by means of suitable
connectivity kernels, that are derived from Lie group theory and are neurally
implemented in long range connectivity in V1. Different kernels are compatible
with the geometric structure of cortical connectivity and they are derived as
the fundamental solutions of the Fokker Planck, the Sub-Riemannian Laplacian
and the isotropic Laplacian equations. The kernels are used to construct
matrices of connectivity among the features present in a visual stimulus.
Global gestalt constraints are then introduced in terms of spectral analysis of
the connectivity matrix, showing that this processing can be cortically
implemented in V1 by mean field neural equations. This analysis performs
grouping of local features and individuates perceptual units with the highest
saliency. Numerical simulations are performed and results are obtained applying
the technique to a number of stimuli.Comment: submitted to Neural Computatio
Unsupervised colour image segmentation by low-level perceptual grouping
This paper proposes a new unsupervised
approach for colour image segmentation. A hierarchy of
image partitions is created on the basis of a function that
merges spatially connected regions according to primary
perceptual criteria. Likewise, a global function that measures the goodness of each defined partition is used to
choose the best low-level perceptual grouping in the hierarchy. Contributions also include a comparative study with
five unsupervised colour image segmentation techniques.
These techniques have been frequently used as a reference
in other comparisons. The results obtained by each method
have been systematically evaluated using four well-known
unsupervised measures for judging the segmentation
quality. Our methodology has globally shown the best
performance, obtaining better results in three out of four of
these segmentation quality measures. Experiments will also
show that our proposal finds low-level perceptual solutions
that are highly correlated with the ones provided by
human
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