By Bernd Jähne

Because the first variation of this booklet used to be released in 1986 it has chanced on its technique to many desks and school rooms. The 5th version has been revised to mirror the improvement of the sector. The presentation and the choice of fabrics is guided via the wishes of a researcher who desires to follow picture processing strategies in his or her box. during this experience the ebook bargains an indispensable view of snapshot processing from photograph acquisition to the extraction of of the knowledge of curiosity. The dialogue of the final options is supplemented with examples from functions on PC-based photograph processing platforms and ready-to-use implementations of vital algorithms. The CD-ROM comprises examples, photographs in addition to workouts for selfstudy in addition to a hand-on model of the picture processing software program heurisko.

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**Example text**

2 shows one and the same image represented with a diﬀerent number of pixels as indicated in the legend. With large pixel sizes (Fig. 2a, b), not only is the spatial resolution poor, but the gray value discontinuities at pixel edges appear as disturbing artifacts distracting us from the content of the image. As the pixels become smaller, the eﬀect becomes less pronounced up to the point where we get the impression of a spatially continuous image. This happens when the pixels become smaller than the spatial resolution of our visual system.

7 million colors (3 channels with 8 bits each), can be displayed on inexpensive PC graphic display systems with a resolution of up to 1600 × 1200 pixels. Consequently, a modern frame grabber no longer requires its own image display unit. It only needs circuits to digitize the electrical signal from the imaging sensor and to store the image in the memory of the computer. The direct transfer of image data from a frame grabber to the memory (RAM) of a microcomputer has become possible since 1995 with the introduction of fast peripheral bus systems such as the PCI bus.

We denote such a basis image with a one at row m, column n by m,n P: m,n pm ,n = 1 m=m ∧n=n 0 otherwise. 10) Any arbitrary scalar image can then be composed from the basis images in Eq. 11) m=0 n=0 where Gm,n denotes the gray value at the position (m, n). It is easy to convince ourselves that the basis images m,n P form an orthonormal base. To that end we require an inner product (also known as scalar product ) which can be deﬁned similarly to the scalar product for vectors. 11: The ﬁrst 56 periodic patterns, the basis images of the Fourier transform, from which the image in Fig.