2016 24th European Signal Processing Conference (EUSIPCO) 2016
DOI: 10.1109/eusipco.2016.7760300
|View full text |Cite
|
Sign up to set email alerts
|

Fully automatic detection of anomalies on wheels surface using an adaptive accurate model and hypothesis testing theory

Abstract: To cite this version:Karim Tout, Rémi Cogranne, Florent Retraint. Fully automatic detection of anomalies on wheels surface using an adaptive accurate model and hypothesis testing theory. ABSTRACTThis paper studies the detection of anomalies, or defects, on wheels' surface. The wheel surface is inspected using an imaging system, placed over the conveyor belt. Due to the nature of the wheels, the different elements are analyzed separately. Because many different types of wheels can be manufactured, it is propose… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

0
5
0

Year Published

2017
2017
2024
2024

Publication Types

Select...
4
2

Relationship

0
6

Authors

Journals

citations
Cited by 6 publications
(5 citation statements)
references
References 11 publications
0
5
0
Order By: Relevance
“…If a feature vector lies outside the hypersphere found by SVDD during testing, the image patch corresponding to this feature vector is considered anomalous. Similar works can be found in [18][19][20]. Compared to these traditional dimensionality reduction models, a convolutional neural network (CNN) provides nonlinear mapping and is better at extracting semantic information.…”
Section: Feature Extraction Based Methodsmentioning
confidence: 81%
“…If a feature vector lies outside the hypersphere found by SVDD during testing, the image patch corresponding to this feature vector is considered anomalous. Similar works can be found in [18][19][20]. Compared to these traditional dimensionality reduction models, a convolutional neural network (CNN) provides nonlinear mapping and is better at extracting semantic information.…”
Section: Feature Extraction Based Methodsmentioning
confidence: 81%
“…Meanwhile the old idea of performing a background subtraction remains quite valid. Indeed, as pointed out still very recently in [137], background subtraction may be used to return to an elementary background model for the residual that might contain only noise.…”
Section: Discussionmentioning
confidence: 99%
“…We need a unified anomaly detection criterion, and we shall see that the a-contrario framework, introduced in Section 3.1, gives one. [38,60,80,105,126,137,141,142] Locally Homogeneous [25,48,51,57,63,67,68,85,88,117,120] Sparsity based [1,9,19,20,31,45,77,78,89,152] Non-local self-similar NL-means inspired [10,33,102,125,132,153] Kernel PCA [62] Diffusion maps [8,29,53,84,95,96,106] 3 Estimating a number of false alarms for all compared methods…”
Section: Conclusion Selection Of the Methods And Their Synthesismentioning
confidence: 99%
See 2 more Smart Citations