A Tool for Science and Technique

From the beginning of science, visual observation has played a major role. At that time, the only way to document the results of an experi- ment was by verbal description and manual drawings. The next major step was the invention of photography which enabled results to be docu- mented objectively. Three prominent examples of scientifi c applications of photography are astronomy, photogrammetry, and particle physics. Astronomers were able to measure positions and magnitudes of stars and photogrammeters produced topographic maps from aerial images. Searching through countless images from hydrogen bubble chambers led to the discovery of many elementary particles in physics. These manual evaluation procedures, however, were time consuming. Some semi- or even fully automated optomechanical devices were designed. However, they were adapted to a single specifi c purpose. This is why quantita- tive evaluation of images did not fi nd widespread application at that time. Generally, images were only used for documentation, qualitative description, and illustration of the phenomena observed.

Nowadays, we are in the middle of a second revolution sparked by the rapid progress in video and computer technology. Personal computers and workstations have become powerful enough to process image data. As a result, multimedia software and hardware is becoming standard for the handling of images, image sequences, and even 3-D visualiza- tion. The technology is now available to any scientist or engineer. In consequence, image processing has expanded and is further rapidly ex- panding from a few specialized applications into a standard scientifi c tool. Image processing techniques are now applied to virtually all the natural sciences and technical disciplines.

A simple example clearly demonstrates the power of visual informa- tion. Imagine you had the task of writing an article about a new technical system, for example, a new type of solar power plant. It would take an enormous eff ort to describe the system if you could not include images and technical drawings. The reader of your imageless article would also have a frustrating experience. He or she would spend a lot of time trying to fi gure out how the new solar power plant worked and might end up with only a poor picture of what it looked like.

 

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B. Jä hne, Digital Image Processing                                                                                                       Copyright © 2002 by Springer-Verlag

ISBN 3–540–67754–2                                                                                                    All rights of reproduction in any form reserved.

4                                                                                         1 Applications and Tools

 

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Figure 1.1: Measurement of particles with imaging techniques: a Bubbles sub- merged by breaking waves using a telecentric illumination and imaging system; from Geiß ler and Jä hne [50]). b Soap bubbles. c Electron microscopy of color pigment particles (courtesy of Dr. Klee, Hoechst AG, Frankfurt).

 

Technical drawings and photographs of the solar power plant would be of enormous help for readers of your article. They would immediately have an idea of the plant and could study details in the drawings and photographs which were not described in the text, but which caught their attention. Pictorial information provides much more detail, a fact which can be precisely summarized by the saying that “a picture is worth a thousand words”. Another observation is of interest. If the reader later heard of the new solar plant, he or she could easily recall what it looked like, the object “solar plant” being instantly associated with an image.

 

Examples of Applications

In this section, examples for scientifi c and technical applications of digi- tal image processing are discussed. The examples demonstrate that im- age processing enables complex phenomena to be investigated, which could not be adequately accessed with conventional measuring tech- niques.

1.2 Examples of Applications                                                           5

 

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Figure 1.2: Industrial parts that are checked by a visual inspection system for the correct position and diameter of holes (courtesy of Martin von Brocke, Robert Bosch GmbH).

 

Counting and Gauging

A classic task for digital image processing is counting particles and mea- suring their size distribution. Figure 1.1 shows three examples with very diff erent particles: gas bubbles submerged by breaking waves, soap bub- bles, and pigment particles. The fi rst challenge with tasks like this is to fi nd an imaging and illumination setup that is well adapted to the mea- suring problem. The bubble images in Fig. 1.1a are visualized by a tele- centric illumination and imaging system. With this setup, the principle rays are parallel to the optical axis. Therefore the size of the imaged bubbles does not depend on their distance. The sampling volume for concentration measurements is determined by estimating the degree of blurring in the bubbles.

It is much more diffi cult to measure the shape of the soap bubbles shown in Fig. 1.1b, because they are transparent. Therefore, deeper lying bubbles superimpose the image of the bubbles in the front layer. More- over, the bubbles show deviations from a circular shape so that suitable parameters must be found to describe their shape.

A third application is the measurement of the size distribution of color pigment particles. This signifi cantly infl uences the quality and properties of paint. Thus, the measurement of the distribution is an important quality control task. The image in Fig. 1.1c taken with a trans- mission electron microscope shows the challenge of this image process- ing task. The particles tend to cluster. Consequently, these clusters have to be identifi ed, and — if possible — to be separated in order not to bias the determination of the size distribution.

Almost any product we use nowadays has been checked for defects by an automatic visual inspection system. One class of tasks includes the checking of correct sizes and positions. Some example images are

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Figure 1.3: Focus series of a press form of PMMA with narrow rectangular holes imaged with a confocal technique using statistically distributed intensity patterns. The images are focused on the following depths measured from the bottom of the holes: a 16 µm, b 480 µm, and c 620 µm (surface of form). d 3-D reconstruction. From Scheuermann et al. [163].

 

shown in Fig. 1.2. Here the position, diameter, and roundness of the holes is checked. Figure 1.2c illustrates that it is not easy to illuminate metallic parts. The edge of the hole on the left is partly bright and thus it is more diffi cult to detect and to measure the holes correctly.

 

Exploring 3-D Space

In images, 3-Dscenes are projected on a 2-Dimage plane. Thus the depth information is lost and special imaging techniques are required to re- trieve the topography of surfaces or volumetric images. In recent years, a large variety of range imaging and volumetric imaging techniques have been developed. Therefore image processing techniques are also applied to depth maps and volumetric images.

Figure 1.3 shows the reconstruction of a press form for microstruc- tures that has been imaged by a special type of confocal microscopy [163]. The form is made out of PMMA, a semi-transparent plastic ma-

1.2 Examples of Applications                                                           7

Figure 1.4: Depth map of a plant leaf measured by optical coherency tomo- graphy (courtesy of Jochen Restle, Robert Bosch GmbH).

 

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Figure 1.5: Magnetic resonance image of a human head: a T1 image; b T2 image (courtesy of Michael Bock, DKFZ Heidelberg).

 

terial with a smooth surface, so that it is almost invisible in standard microscopy. The form has narrow, 500 µm deep rectangular holes.

In order to make the transparent material visible, a statistically dis- tributed pattern is projected through the microscope optics onto the focal plane. This pattern only appears sharp on parts that lie in the fo- cal plane. The pattern gets more blurred with increasing distance from the focal plane. In the focus series shown in Fig. 1.3, it can be seen that fi rst the patterns of the material in the bottom of the holes become sharp (Fig. 1.3a), then after moving the object away from the optics, the fi nal image focuses at the surface of the form (Fig. 1.3c). The depth of the surface can be reconstructed by searching for the position of maximum contrast for each pixel in the focus series (Fig. 1.3d).

Figure 1.4 shows the depth map of a plant leaf that has been imaged with another modern optical 3-D measuring technique known as white-

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Figure 1.6: Growth studies in botany: a Rizinus plant leaf; b map of growth rate; c Growth of corn roots (courtesy of Uli Schurr and Stefan Terjung, Institute of Botany, University of Heidelberg).

 

light interferometry or coherency radar. It is an interferometric tech- nique that uses light with a coherency length of only a few wavelengths. Thus interference patterns occur only with very short path diff erences in the interferometer. This eff ect can be utilized to measure distances with an accuracy in the order of a wavelength of light used.

Magnetic resonance imaging (MR) is an example of a modern volu- metric imaging technique, which we can use to look into the interior of 3-D objects. In contrast to x-ray tomography, it can distinguish diff er- ent tissues such as gray and white brain tissues. Magnetic resonance imaging is a very fl exible technique. Depending on the parameters used, quite diff erent material properties can be visualized (Fig. 1.5).

 

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