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Machine Vision Algorithms and Applications von Steger, Carsten (eBook)

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Machine Vision Algorithms and Applications

The second edition of this successful machine vision textbook is completely updated, revised and expanded by 15% to reflect the developments of recent years in the fields of image acquisition, machine vision algorithms and applications. The new content includes, but is not limited to, a discussion of new cameras and image acquisition interfaces, 3D sensors and technologies, 3D object recognition and 3D image reconstruction. The authors retain their balanced approach with sufficient coverage of the theory and a strong focus on applications. All examples are based on the latest version of the machine vision software HALCON 13, a trial version of which is available from the authors' website. Carsten Steger studied computer science at Technische Universitat Munchen (TUM) and received his PhD from TUM in 1998. In 1996, he co-founded the company MVTec, where he heads the Research and Development department. He has authored and co-authored more than 60 scientific publications in the field of machine vision. Carsten Steger is also a guest lecturer at the Technische Universitat Munchen, where he teaches machine vision. Markus Ulrich studied Geodesy and Remote Sensing at Technische Universitat Munchen (TUM) and received his PhD from TUM in 2003. Since 2003, he is a software engineer at the Research and Development department of MVTec. He has authored and co-authored scientific publications in the fields of photogrammetry and machine vision. Markus Ulrich is also a guest lecturer at the Technische Universitat Munchen, where he teaches close-range photogrammetry. Christian Wiedemann studied Geodesy and Remote Sensing at Technische Universitat Munchen (TUM) and received his PhD from TUM in 2001. He has authored and co-authored more than 40 scientific publications in the fields of photogrammetry, remote sensing, and machine vision. Since 2003, he is a software engineer at the Research and Development department of MVTec.

Produktinformationen

    Format: ePUB
    Kopierschutz: AdobeDRM
    Seitenzahl: 280
    Sprache: Englisch
    ISBN: 9783527812899
    Verlag: Wiley-VCH
    Größe: 24777 kBytes
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Machine Vision Algorithms and Applications

2
Image Acquisition

In this chapter, we will take a look at the hardware components that are involved in obtaining an image of the scene we want to analyze with the algorithms presented in Chapter 3 . Illumination makes the essential features of an object visible. Lenses produce a sharp image on the sensor. The sensor converts the image into a video signal. Finally, camera-computer interfaces (frame grabbers, bus systems like USB, or network interfaces like Ethernet) accept the video signal and convert it into an image in the computer's memory.
2.1 Illumination

The goal of illumination in machine vision is to make the important features of the object visible and to suppress undesired features of the object. To do so, we must consider how the light interacts with the object. One important aspect is the spectral composition of the light and the object. We can use, for example, monochromatic light on colored objects to enhance the contrast of the desired object features. Furthermore, the direction from which we illuminate the object can be used to enhance the visibility of features. We will examine these aspects in this section.
2.1.1 Electromagnetic Radiation

Light is electromagnetic radiation of a certain range of wavelengths, as shown in Table 2.1 . The range of wavelengths visible for humans is 380-780 nm. Electromagnetic radiation with shorter wavelengths is called ultraviolet (UV) radiation. Electromagnetic radiation with even shorter wavelengths consists of X-rays and gamma rays. Electromagnetic radiation with longer wavelengths than the visible range is called infrared (IR) radiation. Electromagnetic radiation with even longer wavelengths consists of microwaves and radio waves.

Monochromatic light is characterized by its wavelength . If light is composed of a range of wavelengths, it is often compared to the spectrum of light emitted by a black body. A black body is an object that absorbs all electromagnetic radiation that falls onto it and thus serves as an ideal source of purely thermal radiation. Therefore, the light spectrum of a black body is directly related to its temperature. The spectral radiance of a black body is given by Planck's law (Planck, 1901; Wyszecki and Stiles, 1982):
(2.1)
Table 2.1 The electromagnetic spectrum relevant for optics and photonics. The names of the ranges for IR and UV radiation correspond to ISO 20473:2007. The names of the colors for visible radiation (light) are due to Lee (2005).
Range Name Abbreviation Wavelength Ultraviolet Extreme UV
Vacuum UV
Deep UV
Mid UV
Near UV -
UV-C
UV-B
UV-A 1 nm-100 nm
100 nm-190 nm
190 nm-280 nm
280 nm-315 nm
315 nm-380 nm Visible Blue-purple
Blue
Green-blue
Blue-green
Green
Yellow-green
Yellow
Orange
Red
Red-purple 380 nm-430 nm
430 nm-480 nm
480 nm-490 nm
490 nm-510 nm
510 nm-530 nm
530 nm-570 nm
570 nm-580 nm
580 nm-600 nm
600 nm-720 nm
720 nm-780 nm Infrared Near IR
Mid IR
Far IR IR-A
IR-B
IR-C 780 nm-1.4 µm
1.4 µm-3 µm
3 µm-50 µm
50 µm-1 mm
Here, c = 2.997 924 58 × 108 m s-1 is the speed of light, h = 6.626 0693 × 10-34 J s is the Planck constant, and k = 1.380 6505 × 10-23 J K-1 is the Boltzmann constant. The spectral radiance is the energy radiated per unit wavelength by an infinitesimal patch of the black body into an infinitesimal solid angle of space. Hence, its unit is W sr-1 m-2 nm-1.

Figure 2.1 displays the spe

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