Zobrazeno 1 - 10
of 39
pro vyhledávání: '"Nyiri, Eric"'
Publikováno v:
Guerin, J., Thiery, S., Nyiri, E., Gibaru, O., & Boots, B. (2021). Combining pretrained CNN feature extractors to enhance clustering of complex natural images. Neurocomputing, 423, 551-571
Recently, a common starting point for solving complex unsupervised image classification tasks is to use generic features, extracted with deep Convolutional Neural Networks (CNN) pretrained on a large and versatile dataset (ImageNet). However, in most
Externí odkaz:
http://arxiv.org/abs/2101.02767
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 has facilitated progress in image understanding, a robot's performance i
Externí odkaz:
http://arxiv.org/abs/1807.10303
Publikováno v:
International Journal of Artificial Intelligence and Applications (IJAIA), March 2018, Volume 9, Number 2
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, the system needs to solve either an instance retrieval (known object
Externí odkaz:
http://arxiv.org/abs/1804.04572
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 details a method to create large datasets for 3D object localization, wit
Externí odkaz:
http://arxiv.org/abs/1707.02978
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 problem of image set clustering for object classification as well as
Externí odkaz:
http://arxiv.org/abs/1707.01700
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 developed to implement a robotics application consisting in sorting and storin
Externí odkaz:
http://arxiv.org/abs/1703.07625
Publikováno v:
Industrial Electronics Society, IECON 2016-42nd Annual Conference of the IEEE Pages 5316--5321
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 for workers in the factory. In this paper, we extend previously submitte
Externí odkaz:
http://arxiv.org/abs/1701.01497
Best $L_1$ approximation of the Heaviside function and best $\ell_1$ approximation of multiscale univariate datasets by cubic splines have a Gibbs phenomenon. Numerical experiments show that it can be reduced by using $L_1$ spline fits which are best
Externí odkaz:
http://arxiv.org/abs/1510.07557
In this article, we study the problem of best $L_1$ approximation of Heaviside-type functions in Chebyshev and weak-Chebyshev spaces. We extend the Hobby-Rice theorem into an appropriate framework and prove the unicity of best $L_1$ approximation of
Externí odkaz:
http://arxiv.org/abs/1407.0228
Publikováno v:
In Computer Aided Geometric Design 2011 28(1):65-74
