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Automation of the diagnosis process in the railway system : Detection of defects in concrete sleepers using vision-based machine learning models

Related publications (141)

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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

Towards Scalable Segmentation of 3D Image Stacks

Carlos Joaquin Becker

Imaging modalities such as Electron Microscopy (EM) and Light Microscopy (LM) can now deliver high-quality, high-resolution image stacks of neural structures. Though these imaging modalities can be used to analyze a variety of components that are critical ...

EPFL2016

Detection of mini-UAVs in the presence of strong topographic relief - a multi-sensor perspective

Yusuf Leblebici

Based on the steadily growing use of mini-UAVs for numerous civilian and military applications, mini-UAVs have been recognized as an increasing potential threat. Therefore, counter-UAV solutions addressing the peculiarities of this class of UAVs have recen ...

Spie-Int Soc Optical Engineering2016

The impact of privacy protection filters on gender recognition

Touradj Ebrahimi

Deep learning-based algorithms have become increasingly efficient in recognition and detection tasks, especially when they are trained on large-scale datasets. Such recent success has led to a speculation that deep learning methods are comparable to or eve ...

2015

Localizing Polygonal Objects in Man-Made Environments

Xiaolu Sun

Object detection is a significant challenge in Computer Vision and has received a lot of attention in the field. One such challenge addressed in this thesis is the detection of polygonal objects, which are prevalent in man-made environments. Shape analysis ...

EPFL2015

Learning to Segments Objects Candidates

Ronan Collobert, Pedro Henrique Oliveira Pinheiro

Recent object detection systems rely on two critical steps: (1) a set of object proposals is predicted as efficiently as possible, and (2) this set of candidate proposals is then passed to an object classifier. Such approaches have been shown they can be f ...

2015

Annihilation-driven Localised Image Edge Models

Martin Vetterli, Thierry Blu, Hanjie Pan

We propose a novel edge detection algorithm with sub-pixel accuracy based on annihilation of signals with finite rate of innovation. We show that the Fourier domain annihilation equations can be interpreted as spatial domain multiplications. From this new ...

IEEE2015

Sample Distillation for Object Detection and Image Classification

François Fleuret, Olivier Canévet, Leonidas Lefakis

We propose a novel approach to efficiently select informative samples for large-scale learning. Instead of directly feeding a learning algorithm with a very large amount of samples, as it is usually done to reach state-of-the-art performance, we have devel ...

2014

Efficient Sample Mining for Object Detection

François Fleuret, Olivier Canévet

2014

Learning Context Cues for Synapse Segmentation

Pascal Fua, Graham Knott, Carlos Joaquin Becker, Karim Ali

We present a new approach for the automated segmentation of synapses in image stacks acquired by Electron Microscopy (EM) that relies on image features specifically designed to take spatial context into account. These features are used to train a classifie ...

Institute of Electrical and Electronics Engineers2013