First-Order Extension of the FLP Stable Model
... 1 (u, v, w))
can be equivalently rewritten without second-order variables
as ¬(p ↔ q) ∧ (r ↔ p ∨ q).
Though rule arrows (←) are treated like usual implications
in the FOL-representation of Π, they are distinguished in the
definition of Π4 (u) because of the presence of ’B∧’ in (7).
If we modify Π4 ( ...
Recognizing solid objects by alignment with an image
... point, for m model points and n image points there are
(~)(~)3! possible different alignments, resulting from
pairing each triple of model points with each triple of
image points. Thus for point features the worst-case
number of hypotheses is O(m3n3). The implementation
that we describe below relies ...
An investigation on local wrinkle-based extractor of age estimation
... non-surgical procedures aimed to improve the appearance of skin (Khavkin and Ellis, 2011). It is also useful in age synthesis and skin rejuvenation. Knowledge of skin histology will deepen the understanding of cutaneous changes associated with ageing and
will promote optimal cosmetic and functional ...
Using Natural Image Priors
... applied to linear filters learned specifically for this task (similar to the exponential family
form in equation 1.2) where the local filters and the nonlinear functions are learned using
contrastive divergence. Given a noisy image, the MAP image can be inferred using
gradient ascent on the posterio ...
Learning with Hierarchical-Deep Models
... superclasses for sharing abstract knowledge among related
classes via a prior on which higher level features are likely
to be distinctive for classes of a certain kind and are thus
likely to support learning new concepts of that kind.
We evaluate the compound HDP-DBM model on three
different percept ...
Poster - The University of Manchester
... I From the joint likelihood we derive information theoretic criteria which
maximise the likelihood with respect to the selected features.
I We show that the IAMB algorithm  for Markov Blanket discovery also
optimises this model, using a sparsity prior.
I Finally we extend IAMB to include a domain ...
various object recognition techniques for computer vision
... sizes / scale or even when they are translated or rotated. Objects can even be
recognized when they are partially obstructed from view. This task is still a
challenge for computer vision systems. Recognition remains challenging in
large part due to the significant variations exhibited by real-world ...
Unsupervised Object Counting without Object Recognition
... a straightforward approach would be to perform explicit
object detection , , –. Also, regression-based
approaches have been proposed, which translate the image
features into the number of objects with a regression model ,
, . These approaches require labeled training data,
Using fuzzy temporal logic for monitoring behavior
... value of the proposition V isibleBall will be a real number between 0 and 1 reflecting our incapacity to draw clear
boundaries between thruthness and falseness of a proposition. This allows us to include fuzzy statements such as
“slightly visible” or “completely visible”. On the other
hand, it is no ...
A Decision Procedure for a Fragment of Linear Time Mu
... Gµ formulas and prove that every closed formula can be transformed into this form. GPF decomposes a formula into the
present and future parts. The present part is the conjunction
of atomic propositions or their negations while the future part
is the conjunction of elements in the closure of a given ...
from Converse PDL - School of Computer Science
... We show that is possible to eliminate the "converse" operator from CPDL,
without compromising the soundness and completeness of inference for it. Specifically we present an intuitive encoding of CPDL formulae into PDL that eliminates
the converse programs from a CPDL formula, but adds enough informa ...
Learning to Parse Images
... Neal  introduced generative models composed of multiple layers of stochastic logistic units connected in a directed acyclic graph. In general, as each unit has
multiple parents, it is intractable to compute the posterior distribution over hidden
variables when certain variables are observed. Howe ...
One-shot learning is an object categorization problem of current research interest in computer vision. Whereas most machine learning based object categorization algorithms require training on hundreds or thousands of images and very large datasets, one-shot learning aims to learn information about object categories from one, or only a few, training images. The primary focus of this article will be on the solution to this problem presented by L. Fei-Fei, R. Fergus and P. Perona in IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol28(4), 2006, which uses a generative object category model and variational Bayesian framework for representation and learning of visual object categories from a handful of training examples. Another paper, presented at the International Conference on Computer Vision and Pattern Recognition (CVPR) 2000 by Erik Miller, Nicholas Matsakis, and Paul Viola will also be discussed.