Zobrazeno 1 - 10
of 43
pro vyhledávání: '"Gutierrez, Pierre"'
Autor:
Forestier, Yann
Publikováno v:
Histoire de l'éducation, 2016 Jan 01(146), 111-114.
Externí odkaz:
https://www.jstor.org/stable/26381884
Automating quality inspection with computer vision techniques is often a very data-demanding task. Specifically, supervised deep learning requires a large amount of annotated images for training. In practice, collecting and annotating such data is no
Externí odkaz:
http://arxiv.org/abs/2202.12818
Anomaly detection has recently seen great progress in the field of visual inspection. More specifically, the use of classical outlier detection techniques on features extracted by deep pre-trained neural networks have been shown to deliver remarkable
Externí odkaz:
http://arxiv.org/abs/2202.12759
The use of deep features coming from pre-trained neural networks for unsupervised anomaly detection purposes has recently gathered momentum in the computer vision field. In particular, industrial inspection applications can take advantage of such fea
Externí odkaz:
http://arxiv.org/abs/2106.01277
Because manufacturing processes evolve fast, and since production visual aspect can vary significantly on a daily basis, the ability to rapidly update machine vision based inspection systems is paramount. Unfortunately, supervised learning of convolu
Externí odkaz:
http://arxiv.org/abs/2104.02973
Autor:
Gutierrez, Pierre, Luschkova, Maria, Cordier, Antoine, Shukor, Mustafa, Schappert, Mona, Dahmen, Tim
Deep learning is now the gold standard in computer vision-based quality inspection systems. In order to detect defects, supervised learning is often utilized, but necessitates a large amount of annotated images, which can be costly: collecting, clean
Externí odkaz:
http://arxiv.org/abs/2104.02980
Our team won the second prize of the Safe Aging with SPHERE Challenge organized by SPHERE, in conjunction with ECML-PKDD and Driven Data. The goal of the competition was to recognize activities performed by humans, using sensor data. This paper prese
Externí odkaz:
http://arxiv.org/abs/1610.02757
Given a data set with many features observed in a large number of conditions, it is desirable to fuse and aggregate conditions which are similar to ease the interpretation and extract the main characteristics of the data. This paper presents a multid
Externí odkaz:
http://arxiv.org/abs/1407.5915
Publikováno v:
Journal of Computational and Graphical Statistics, 2017 Mar 01. 26(1), 205-216.
Externí odkaz:
https://www.jstor.org/stable/44861944
Publikováno v:
In Urban Climate June 2016 16:43-58