48 citations · 52 across the 3 of their papers we have counts for
7 papers
Combining pretrained CNN feature extractors to enhance clustering of complex natural images
Joris Guerin, Stephane Thiery, Eric Nyiri +2
Recently, a common starting point for solving complex unsupervised image classification tasks is to use generic features, extracted with deep Convolutional Neural Networks (CNN) pr…
Semantically Meaningful View Selection
Joris Guérin, Olivier Gibaru, Eric Nyiri +2
An understanding of the nature of objects could help robots to solve both high-level abstract tasks and improve performance at lower-level concrete tasks. Although deep learning ha…
Unsupervised robotic sorting: Towards autonomous decision making robots
Joris Guérin, Stéphane Thiery, Eric Nyiri +1
Autonomous sorting is a crucial task in industrial robotics which can be very challenging depending on the expected amount of automation. Usually, to decide where to sort an object…
Automatic Construction of Real-World Datasets for 3D Object Localization using Two Cameras
Joris Guérin, Olivier Gibaru, Eric Nyiri +1
Unlike classification, position labels cannot be assigned manually by humans. For this reason, generating supervision for precise object localization is a hard task. This paper det…
CNN features are also great at unsupervised classification
Joris Guérin, Olivier Gibaru, Stéphane Thiery +1
This paper aims at providing insight on the transferability of deep CNN features to unsupervised problems. We study the impact of different pretrained CNN feature extractors on the…
Clustering for Different Scales of Measurement - the Gap-Ratio Weighted K-means Algorithm
Joris Guérin, Olivier Gibaru, Stéphane Thiery +1
This paper describes a method for clustering data that are spread out over large regions and which dimensions are on different scales of measurement. Such an algorithm was develope…
Learning local trajectories for high precision robotic tasks : application to KUKA LBR iiwa Cartesian positioning
Joris Guerin, Olivier Gibaru, Eric Nyiri +1
To ease the development of robot learning in industry, two conditions need to be fulfilled. Manipulators must be able to learn high accuracy and precision tasks while being safe fo…