Master Thesis - Fachbereich Informatik

1. Introduction
Heat shrinkable tubing is widely used for electrical and mechanical insulation, sealing,
identification and connection solutions. Customers are mainly from the automotive, electronics,
military or aerospace sector. In terms of competition in world markets, high
quality assurance standards are essential in establishing and maintaining customer relationships.
Especially in the automotive supply industry, accuracy demands are very high,
and tolerated outliers are specified in only a few parts-per-million.
In this master thesis, a prototype of a vision-based sensor for real-time length measurement
of heat shrink tubes in line production is presented. The main objectives are
accuracy, reliability and meeting time constraints.
The thesis work has been accomplished in cooperation with the company DSG-Canusa,
Meckenheim, Germany.
1.1. Machine Vision - State of Art
This section gives an overview on the term Machine Vision (MV), the use of vision systems
in industrial applications, and a brief historical review. In addition, the advantages and
drawbacks of MV are discussed and related applications are presented. The term Machine
Vision is defined by Davies [16] as follows:
“Machine Vision is the study of methods and techniques whereby artificial vision systems
can be constructed and usefully employed in practical applications. As such, it
embraces both the science and engineering of vision.”
Researchers and engineers argue whether the terms Machine Vision and Computer
Vision can be used synonymously [7]. Both terms are part of a larger field called Artificial
Vision and have many things in common. The main objective is to make artificial systems
‘see’. However, the priorities of the two subjects differ.
Computer Vision has arisen in the academic field and concentrates mainly on theoretical
problems with a strong mathematical background. Usually, as the term Computer Vision
indicates, a computer processes an input image or a sequence of images. Nevertheless,
many methods and algorithms developed in Computer Vision can be adapted to practical
applications.
Machine Vision, on the other hand, implies practical solutions for many applications,
and covers not only the image processing itself, but also the engineering that makes a
system work [16]. This includes the right choice of the sensor, optics, illumination, etc.
MV systems are often used in industrial environments making robustness, reliability and
cost-effectiveness very important. If an application is highly time-constrained or computationally
expensive, specific hardware (e.g. DSPs, ASICs, or FPGAs) is used instead of
an off-the-shelf computer [42]. A current trend is to develop imaging sensors, that have
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