Result Details
Intelligent Defect and Anomaly Detection System - A Study in Tofu
HRADIŠ, M. Intelligent Defect and Anomaly Detection System - A Study in Tofu. Brno: Fingera s.r.o., 2015. p. 1-35.
Type
summary research report
Language
English
Authors
Hradiš Michal, Ing., Ph.D., DCGM (FIT)
Abstract
The report proposes an intelligent system for detection of defects in images based on a number of computer vision and pattern recognition methods: classification, segmentation, anomaly detection, and classification support visualization.
The components were tested on real-life data from a food packaging plant.
The studied methods have to be able to detect physical defects and anomalies of various types of products and they should be able to adapt to new types of products and defects automatically or by users supervision. The user should be able to guide the detection system by providing examples of defects, by correcting wrong detections of the automated system, or by similar actions.
Keywords
anomaly detection, defect detection, visual quality control, semi-supervised learning, active learning, convolutional neural networks
Published
2015
Pages
1–35
Publisher
Fingera s.r.o.
Place
Brno
BibTeX
@misc{BUT123629,
author="Michal {Hradiš}",
title="Intelligent Defect and Anomaly Detection System - A Study in Tofu",
year="2015",
pages="1--35",
publisher="Fingera s.r.o.",
address="Brno",
note="Summary research report"
}
Projects
Intelligent visual quality control, Inkubátor VUT, start: 2015-06-01, end: 2015-12-31, completed
Research groups
Computer Graphics Research Group (RG GRAPH)
Departments