19,723 research outputs found
Multiscale Discriminant Saliency for Visual Attention
The bottom-up saliency, an early stage of humans' visual attention, can be
considered as a binary classification problem between center and surround
classes. Discriminant power of features for the classification is measured as
mutual information between features and two classes distribution. The estimated
discrepancy of two feature classes very much depends on considered scale
levels; then, multi-scale structure and discriminant power are integrated by
employing discrete wavelet features and Hidden markov tree (HMT). With wavelet
coefficients and Hidden Markov Tree parameters, quad-tree like label structures
are constructed and utilized in maximum a posterior probability (MAP) of hidden
class variables at corresponding dyadic sub-squares. Then, saliency value for
each dyadic square at each scale level is computed with discriminant power
principle and the MAP. Finally, across multiple scales is integrated the final
saliency map by an information maximization rule. Both standard quantitative
tools such as NSS, LCC, AUC and qualitative assessments are used for evaluating
the proposed multiscale discriminant saliency method (MDIS) against the
well-know information-based saliency method AIM on its Bruce Database wity
eye-tracking data. Simulation results are presented and analyzed to verify the
validity of MDIS as well as point out its disadvantages for further research
direction.Comment: 16 pages, ICCSA 2013 - BIOCA sessio
Convolutional Patch Networks with Spatial Prior for Road Detection and Urban Scene Understanding
Classifying single image patches is important in many different applications,
such as road detection or scene understanding. In this paper, we present
convolutional patch networks, which are convolutional networks learned to
distinguish different image patches and which can be used for pixel-wise
labeling. We also show how to incorporate spatial information of the patch as
an input to the network, which allows for learning spatial priors for certain
categories jointly with an appearance model. In particular, we focus on road
detection and urban scene understanding, two application areas where we are
able to achieve state-of-the-art results on the KITTI as well as on the
LabelMeFacade dataset.
Furthermore, our paper offers a guideline for people working in the area and
desperately wandering through all the painstaking details that render training
CNs on image patches extremely difficult.Comment: VISAPP 2015 pape
Blazar Flaring Patterns (B-FlaP): Classifying Blazar Candidates of Uncertain type in the third Fermi-LAT catalog by Artificial Neural Networks
The Fermi Large Area Telescope (LAT) is currently the most important facility
for investigating the GeV -ray sky. With Fermi LAT more than three
thousand -ray sources have been discovered so far. 1144 () of
the sources are active galaxies of the blazar class, and 573 () are
listed as Blazar Candidate of Uncertain type (BCU), or sources without a
conclusive classification. We use the Empirical Cumulative Distribution
Functions (ECDF) and the Artificial Neural Networks (ANN) for a fast method of
screening and classification for BCUs based on data collected at -ray
energies only, when rigorous multiwavelength analysis is not available. Based
on our method, we classify 342 BCUs as BL Lacs and 154 as FSRQs, while 77
objects remain uncertain. Moreover, radio analysis and direct observations in
ground-based optical observatories are used as counterparts to the statistical
classifications to validate the method. This approach is of interest because of
the increasing number of unclassified sources in Fermi catalogs and because
blazars and in particular their subclass High Synchrotron Peak (HSP) objects
are the main targets of atmospheric Cherenkov telescopes.Comment: 18 pages, 17 figures, accepted for publication on MNRA
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