671,418 research outputs found
Advances in Hyperspectral Image Classification: Earth monitoring with statistical learning methods
Hyperspectral images show similar statistical properties to natural grayscale
or color photographic images. However, the classification of hyperspectral
images is more challenging because of the very high dimensionality of the
pixels and the small number of labeled examples typically available for
learning. These peculiarities lead to particular signal processing problems,
mainly characterized by indetermination and complex manifolds. The framework of
statistical learning has gained popularity in the last decade. New methods have
been presented to account for the spatial homogeneity of images, to include
user's interaction via active learning, to take advantage of the manifold
structure with semisupervised learning, to extract and encode invariances, or
to adapt classifiers and image representations to unseen yet similar scenes.
This tutuorial reviews the main advances for hyperspectral remote sensing image
classification through illustrative examples.Comment: IEEE Signal Processing Magazine, 201
The LAOG-Planet Imaging Surveys
With the development of high contrast imaging techniques and infrared
detectors, vast efforts have been devoted during the past decade to detect and
characterize lighter, cooler and closer companions to nearby stars, and
ultimately image new planetary systems. Complementary to other observing
techniques (radial velocity, transit, micro-lensing, pulsar-timing), this
approach has opened a new astrophysical window to study the physical properties
and the formation mechanisms of brown dwarfs and planets. I here will briefly
present the observing challenge, the different observing techniques, strategies
and samples of current exoplanet imaging searches that have been selected in
the context of the LAOG-Planet Imaging Surveys. I will finally describe the
most recent results that led to the discovery of giant planets probably formed
like the ones of our solar system, offering exciting and attractive
perspectives for the future generation of deep imaging instruments.Comment: 6 pages, 5 figures, Invited talk of "Exoplanets and disks: their
formation and diversity" conference, 9-12 March 200
Cellular neural networks, Navier-Stokes equation and microarray image reconstruction
Copyright @ 2011 IEEE.Although the last decade has witnessed a great deal of improvements achieved for the microarray technology, many major developments in all the main stages of this technology, including image processing, are still needed. Some hardware implementations of microarray image processing have been proposed in the literature and proved to be promising alternatives to the currently available software systems. However, the main drawback of those proposed approaches is the unsuitable addressing of the quantification of the gene spot in a realistic way without any assumption about the image surface. Our aim in this paper is to present a new image-reconstruction algorithm using the cellular neural network that solves the Navier–Stokes equation. This algorithm offers a robust method for estimating the background signal within the gene-spot region. The MATCNN toolbox for Matlab is used to test the proposed method. Quantitative comparisons are carried out, i.e., in terms of objective criteria, between our approach and some other available methods. It is shown that the proposed algorithm gives highly accurate and realistic measurements in a fully automated manner within a remarkably efficient time
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