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Medical Image Classification Using Generalized Zero Shot Learning

Related publications (39)

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A new smoothness based strategy for semi-supervised atmospheric correction: application to the Léman-Baïkal campaign

Bertrand Merminod, Manuel Simon Paul Cubero-Castan, Yosef Akhtman, Dragos Constantin, Vincent Maurice Nouchi, Kevin Sylvain Barbieux

The estimation of reflectance from hyperspectral data, a process called atmospheric correction, is a critical first step. Most of the methods need measurements of ground reflectances or atmosphere. When no information is available, unsupervised methods as ...

2015

Sampling Spherical Finite Rate of Innovation Signals

Yue Lu, Ivan Dokmanic

Sampling schemes on the sphere require O(L^2) samples to perfectly sample and reconstruct a signal with bandwidth L. If the signal in question is a low-pass observation of a finite collection of spikes or rotations of a known function, we can use less samp ...

2015

The main challenge of new information technologies is to retrieve intelligible information from the large volume of digital data gathered every day. Among the variety of existing data sources, the satellites continuously observing the surface of the Earth ...

EPFL2014

Sparse Binary Features for Image Classification

Johann Paratte

In this work a new method for automatic image classification is proposed. It relies on a compact representation of images using sets of sparse binary features. This work first evaluates the Fast Retina Keypoint binary descriptor and proposes imp ...

2013

Graph Matching for Adaptation in Remote Sensing

Devis Tuia

We present an adaptation algorithm focused on the description of the data changes under different acquisition conditions. When considering a source and a destination domain, the adaptation is carried out by transforming one data set to the other using an a ...

Ieee-Inst Electrical Electronics Engineers Inc2013

FlowBoost - Appearance Learning from Sparsely Annotated Video

François Fleuret, Karim Ali, David Hasler

We propose a new learning method which exploits temporal consistency to successfully learn a complex appearance model from a sparsely labeled training video. Our approach consists in iteratively improving an appearance based model built with a Boosting pro ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2011

Over the past few decades we have been experiencing a data explosion; massive amounts of data are increasingly collected and multimedia databases, such as YouTube and Flickr, are rapidly expanding. At the same time rapid technological advancements in mobil ...

EPFL2009

Learning and recovering 3D surface deformations

Mathieu Salzmann

Recovering the 3D deformations of a non-rigid surface from a single viewpoint has applications in many domains such as sports, entertainment, and medical imaging. Unfortunately, without any knowledge of the possible deformations that the object of interest ...

EPFL2009

Dense deformation field estimation for atlas registration using the active contour framework

Valérie Duay

A key research area in computer vision is image segmentation. Image segmentation aims at extracting objects of interest in images or video sequences. These objects contain relevant information for a given application. For example, a video surveillance appl ...

EPFL2008

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NOVELTY - The display method involves evaluating the sequence of images by: associating a first analysis image value to pixels in the first sequence of images to which pixels of same locations of the field (11) are imaged, respectively; associating a secon ...

2008