1,749 research outputs found
Stochastic spectral-spatial permutation ordering combination for nonlocal morphological processing
International audienceThe extension of mathematical morphology to mul-tivariate data has been an active research topic in recent years. In this paper we propose an approach that relies on the consensus combination of several stochastic permutation orderings. The latter are obtained by searching for a smooth shortest path on a graph representing an image. The construction of the graph can be based on both spatial and spectral information and naturally enables patch-based nonlocal processing
Hilbert geometry of the Siegel disk: The Siegel-Klein disk model
We study the Hilbert geometry induced by the Siegel disk domain, an open
bounded convex set of complex square matrices of operator norm strictly less
than one. This Hilbert geometry yields a generalization of the Klein disk model
of hyperbolic geometry, henceforth called the Siegel-Klein disk model to
differentiate it with the classical Siegel upper plane and disk domains. In the
Siegel-Klein disk, geodesics are by construction always unique and Euclidean
straight, allowing one to design efficient geometric algorithms and
data-structures from computational geometry. For example, we show how to
approximate the smallest enclosing ball of a set of complex square matrices in
the Siegel disk domains: We compare two generalizations of the iterative
core-set algorithm of Badoiu and Clarkson (BC) in the Siegel-Poincar\'e disk
and in the Siegel-Klein disk: We demonstrate that geometric computing in the
Siegel-Klein disk allows one (i) to bypass the time-costly recentering
operations to the disk origin required at each iteration of the BC algorithm in
the Siegel-Poincar\'e disk model, and (ii) to approximate fast and numerically
the Siegel-Klein distance with guaranteed lower and upper bounds derived from
nested Hilbert geometries.Comment: 42 pages, 7 figure
Model-based learning of local image features for unsupervised texture segmentation
Features that capture well the textural patterns of a certain class of images
are crucial for the performance of texture segmentation methods. The manual
selection of features or designing new ones can be a tedious task. Therefore,
it is desirable to automatically adapt the features to a certain image or class
of images. Typically, this requires a large set of training images with similar
textures and ground truth segmentation. In this work, we propose a framework to
learn features for texture segmentation when no such training data is
available. The cost function for our learning process is constructed to match a
commonly used segmentation model, the piecewise constant Mumford-Shah model.
This means that the features are learned such that they provide an
approximately piecewise constant feature image with a small jump set. Based on
this idea, we develop a two-stage algorithm which first learns suitable
convolutional features and then performs a segmentation. We note that the
features can be learned from a small set of images, from a single image, or
even from image patches. The proposed method achieves a competitive rank in the
Prague texture segmentation benchmark, and it is effective for segmenting
histological images
A spectral graph theoretic approach to quantification and calibration of collective morphological differences in cell images
Motivation: High-throughput image-based assay technologies can rapidly produce a large number of cell images for drug screening, but data analysis is still a major bottleneck that limits their utility. Quantifying a wide variety of morphological differences observed in cell images under different drug influences is still a challenging task because the result can be highly sensitive to sampling and noise
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
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