**1. Introduction**

130 Advanced Topics in Measurements

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Deutscher Forstwirtschaftsrat e.V.& Verband der Deutschen Säge- und Holzindustrie e.V.

Hauffe, P.& Müller, L. (2002). Rundholzvermessung in Europa vereinheitlichen. *Holz-*

Hunková, V. (2011). *Faktory ovlivňující přejímku kulatiny při elektronickém měření jejích rozměrů*.

Janák, K. (2007). Differences in round wood measurements using electronic 2D and 3D

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*Zentralblatt* N. 77, 28. 6. 2002, p. 948

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**8. References** 

14.

Czech Republic

133), ISSN 0012-6772

Republic

Vienna, Austria

Many of the techniques of digital image processing, or digital picture processing as it often was called, were developed in the 1960s at the Jet Propulsion Laboratory, Massachusetts Institute of Technology, Bell Laboratories, University of Maryland, and a few other research facilities, with application to satellite imagery, wire-photo standards conversion, medical imaging, videophone, character recognition, and photograph enhancement. The cost of processing was fairly high, however, with the computing equipment of that era. That changed in the 1970s, when digital image processing proliferated as cheaper computers and dedicated hardware became available. Images then could be processed in real time. As general-purpose computers became faster, they started to take over the role of dedicated hardware for all but the most specialized and computer-intensive operations (Anonymous, 2011b). With the fast computers and signal processors available in the 2000s, digital image processing has become the most common form of image processing and generally, is used because it is not only the most versatile method, but also the cheapest. Digital image processing technology for medical applications was inducted into the Space Foundation Space Technology Hall of Fame in 1994.

Today image processing is used in a wide variety of applications for two somewhat different purposes: improving the visual appearance of images to a human viewer, preparing images for the measurement of the features and structures that they reveal. The techniques that are appropriate for each of these tasks are not always the same. This chapter covers methods that are used for measurement tasks.

A machine vision system processes images acquired from an electronic unit, which is like the human vision system where the brain processes images derived from the eyes. Machine vision is a rich and rewarding topic for study and research for agriculture engineers, electronic engineers, computer scientists and many others. Increasingly, it has a commercial future. There are many vision systems in routine industrial use: cameras inspect mechanical parts to check size, food is inspected for quality and images used in biometrics benefit from machine vision techniques (Nixon & Aguado, 2002 ).

Imaging systems cover all processes involved in the formation of an image from objects and the sensors that convert radiation into electric signals, and further into digital signals that

Machine Vision Measurement Technology and Agricultural Applications 133

In recent years, a massive research and development effort has been witnessed in colour imaging technologies in both industry and ordinary life. Colour is commonly used in television, computer displays, cinema motion pictures, print and photographs. In all these application areas, the perception of colour is paramount for the correct understanding and dissemination of the visual information. Recent technological advances have reduced the complexity and the cost of colour devices, such as monitors, printers, scanners and copiers, thus allowing their use in the office and home environment. However, it is the extreme and still increasing popularity of the consumer, single-sensor digital cameras that today boosts the research activities in the field of digital colour image acquisition, processing and storage. Single-sensor camera image processing methods are becoming more important due to the development and proliferation of emerging digital camerabased applications and commercial devices, such as imaging enabled mobile phones and personal digital assistants, sensor networks, surveillance and automotive apparatus

Digital colour cameras capture colour images of real-life scenes electronically using an image sensor, usually a charge-coupled device (CCD), or complementary metal oxide semiconductor (CMOS), sensor, instead of the film used in the conventional analog cameras. Therefore, captured photos can be immediately viewed by the user on the digital camera's display, and immediately stored, processed, or transmitted without any doubt, this is one of

This architecture reduces cost by placing a colour filter array (CFA), which is a mosaic of colour filters, on top of the conventional single CCD/CMOS image sensor to capture all

the most attractive features of digital imaging (Lukac & Plataniotis, 2007).

Fig. 1. Minolta Cr 200 colourmeter (Beyaz, 2009a).

**2.2 Camera image** 

(Lukac & Plataniotis, 2007).

**2.2.2 Single-sensor device** 

**2.2.1 Digital camera architectures** 

can be processed by a computer. Generally the goal is to attain a signal from an object in such a form that we know where it is (geometry) and what it is or what properties it has (Jähne & Haußecker, 2000).

The human visual system is limited to a very narrow portion of the spectrum of electromagnetic radiation, called visible light. Image processes are sensitive to wavelengths and additional information might be hidden in the spectral distribution of radiation. Using different types of radiation allows taking images from different depths or different object properties (Nixon & Aguado, 2002 ).

The measurement of images is often a principal method for acquiring scientific data and generally requires that features or structure be well defined, either by edges or unique colour, texture, or some combination of these factors. The types of measurements that can be performed on entire scenes or on individual features are important in determining the appropriate processing steps (Russ, 2006).
