757 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
No-reference image quality assessment through the von Mises distribution
An innovative way of calculating the von Mises distribution (VMD) of image
entropy is introduced in this paper. The VMD's concentration parameter and some
fitness parameter that will be later defined, have been analyzed in the
experimental part for determining their suitability as a image quality
assessment measure in some particular distortions such as Gaussian blur or
additive Gaussian noise. To achieve such measure, the local R\'{e}nyi entropy
is calculated in four equally spaced orientations and used to determine the
parameters of the von Mises distribution of the image entropy. Considering
contextual images, experimental results after applying this model show that the
best-in-focus noise-free images are associated with the highest values for the
von Mises distribution concentration parameter and the highest approximation of
image data to the von Mises distribution model. Our defined von Misses fitness
parameter experimentally appears also as a suitable no-reference image quality
assessment indicator for no-contextual images.Comment: 29 pages, 11 figure
Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14)
The implicit objective of the biennial "international - Traveling Workshop on
Interactions between Sparse models and Technology" (iTWIST) is to foster
collaboration between international scientific teams by disseminating ideas
through both specific oral/poster presentations and free discussions. For its
second edition, the iTWIST workshop took place in the medieval and picturesque
town of Namur in Belgium, from Wednesday August 27th till Friday August 29th,
2014. The workshop was conveniently located in "The Arsenal" building within
walking distance of both hotels and town center. iTWIST'14 has gathered about
70 international participants and has featured 9 invited talks, 10 oral
presentations, and 14 posters on the following themes, all related to the
theory, application and generalization of the "sparsity paradigm":
Sparsity-driven data sensing and processing; Union of low dimensional
subspaces; Beyond linear and convex inverse problem; Matrix/manifold/graph
sensing/processing; Blind inverse problems and dictionary learning; Sparsity
and computational neuroscience; Information theory, geometry and randomness;
Complexity/accuracy tradeoffs in numerical methods; Sparsity? What's next?;
Sparse machine learning and inference.Comment: 69 pages, 24 extended abstracts, iTWIST'14 website:
http://sites.google.com/site/itwist1
Analyse d'images de documents anciens : Catégorisation de contenus par approche texture
Nous proposons une caractérisation du contenu des ouvrages anciens basée sur une approche texture non paramétrique. Cette démarche se veut générique et adaptable à tout type d'ouvrages en s'appuyant sur l'homogénéité des textures que l'on retrouve dans un ouvrage. En appliquant à plusieurs résolutions 5 algorithmes d'extractions de textures il est possible de caractériser le contenu des pages d'un ouvrage. Cette méthode est appliquée sur des pages d'ouvrages anciens du 16ème siècle
- …