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Fidelity Estimation Improves Noisy-Image Classification with Pretrained Networks

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Vision-based detection of aircrafts and UAVs

Artem Rozantsev

Unmanned Aerial Vehicles are becoming increasingly popular for a broad variety of tasks ranging from aerial imagery to objects delivery. With the expansion of the areas, where drones can be efficiently used, the collision risk with other flying objects inc ...

EPFL2017

Learning Aerial Image Segmentation from Online Maps

Martin Jaggi, Jan Dirk Wegner, Aurélien Lucchi, Thomas Hofmann

This study deals with semantic segmentation of high-resolution (aerial) images where a semantic class label is assigned to each pixel via supervised classification as a basis for automatic map generation. Recently, deep convolutional neural networks (CNNs) ...

Institute of Electrical and Electronics Engineers2017

Visual Analysis of Maya Glyphs via Crowdsourcing and Deep Learning

Gülcan Can

In this dissertation, we study visual analysis methods for complex ancient Maya writings. The unit sign of a Maya text is called glyph, and may have either semantic or syllabic significance. There are over 800 identified glyph categories, and over 1400 var ...

EPFL2017

Diffusion gradient boosting for networked learning

Ali H. Sayed, Bicheng Ying

Using duality arguments from optimization theory, this work develops an effective distributed gradient boosting strategy for inference and classification by networked clusters of learners. By sharing local dual variables with their immediate neighbors thro ...

2017

Machine Vision algorithms on cadaster plans

Sofia Ares Oliveira

Cadaster plans are cornerstones for reconstructing dense representations of the history of the city. However, as some of these handwritten documents are more than 200 years old, the establishment of processing pipeline for interpreting them remains extreme ...

2017

Networks on Chips: 15 Years Later

Giovanni De Micheli, Luca Benini

The authors of a Computer article from 2002 reflect on their proposal to use networks on chips to address scalable communications on silicon VLSI chips. ...

2017

Using Deep Learning for Image-Based Plant Disease Detection

Marcel Salathé, Sharada Prasanna Mohanty

Crop diseases are a major threat to food security, but their rapid identification remains difficult in many parts of the world due to the lack of the necessary infrastructure. The combination of increasing global smartphone penetration and recent advances ...

Frontiers Media Sa2016

Word Sequence Modeling using Deep Learning

Joël Yvon Roland Legrand

For a long time, natural language processing (NLP) has relied on generative models with task specific and manually engineered features. Recently, there has been a resurgence of interest for neural networks in the machine learning community, obtaining state ...

EPFL2016

A Deeper Look at Dataset Bias

Barbara Caputo, Tatiana Tommasi, Novi Patricia

The presence of a bias in each image data collection has recently attracted a lot of attention in the computer vision community showing the limits in generalization of any learning method trained on a specific dataset. At the same time, with the rapid deve ...

Springer Int Publishing Ag2015

Learning Separable Filters

Pascal Fua, Vincent Lepetit, Bugra Tekin, Roberto Rigamonti, Amos Sironi

Learning filters to produce sparse image representations in terms of overcomplete dictionaries has emerged as a powerful way to create image features for many different purposes. Unfortunately, these filters are usually both numerous and non-separable, mak ...

Ieee Computer Soc2015