A DEFECT DETECTION SCHEME FOR WEB SURFACE INSPECTION-Reference-Cited by-同舟云学术

A DEFECT DETECTION SCHEME FOR WEB SURFACE INSPECTION

Published:2000-09 Issue:06 Volume:14 Page:735-755
ISSN:0218-0014
Container-title:International Journal of Pattern Recognition and Artificial Intelligence
language:en
Short-container-title:Int. J. Patt. Recogn. Artif. Intell.

Author:

IIVARINEN JUKKA¹,HEIKKINEN KATRIINA²,RAUHAMAA JUHANI³,VUORIMAA PETRI⁴,VISA ARI⁵

Affiliation:

1. Helsinki University of Technology, Laboratory of Computer and Information Science, P.O. Box 5400, FIN-02150 HUT, Finland

2. Tampere University of Technology, Digital and Computer Systems Laboratory, Finland

3. ABB Industry Oy, P.O. Box 94, FIN-00381 Helsinki, Finland

4. Helsinki University of Technology, Telecommunications Software and Multimedia Laboratory, P.O. Box 5400, FIN-02150 HUT, Finland

5. Tampere University of Technology, Signal Processing Laboratory, P.O. Box 553, FIN-33101 Tampere, Finland

Abstract

The goal of this work was to develop an improved defect detection scheme for high-speed real-time web surface inspection. This goal was realized by splitting the task into two independent parts: feature extraction and segmentation. Both parts were implemented using efficient algorithms which were implemented in hardware that is suitable and fast enough to be included in a working web inspection system. The proposed scheme is based on some derived texture features and a new self-organizing map variant, the statistical self-organizing map. These techniques offer several improvements over the gray-level thresholding techniques that have been traditionally used in commercial web inspection systems.

Publisher

World Scientific Pub Co Pte Lt

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Software

Link

https://www.worldscientific.com/doi/pdf/10.1142/S0218001400000507

Reference15 articles.

1. Designing a defect classification system: A case study

2. A Theoretical Comparison of Texture Algorithms

3. Toward a structural textural analyzer based on statistical methods

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