Abstract
Intensity based images are the most common input data structure for image processing and analysis. In practice, matrices are used for the representation of these discrete gray-level images. Each element of the two-dimensional matrix describes the gray-level of the digital image at its associated location. These “picture elements” are called pixels.
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Due to the optical measurement device used in this case, there are areas on the object for which no range value is computed (like a shadow on the left).
Ref. to Figure 11.3) Images by the Institute for Physics, University of Erlangen—Nürnberg
Pixels may be either quadratic (the rare case), or rectangular depending on the layout of the CCD. The relation of the sides is stored in the scaling factor (c.f. Exercise 1.g).
This means that f (x, y) = f yz = f ij . You should try to be consistent in your programs with respect to argument orderings and variable names!
This is the test for equality is a complicated topic which will not be discussed here. It is different from the test for identity (isSame).
Of course, the color images are printed here as gray—levels. They are available in full color together with the course material (Appendix B).
Would you prefer a matrix of a structure containing three bytes for each pixel? Discuss advantages and disadvantages!
The image in Figure 11.2 was created from the color image in Figure 11.5 using this formula.
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© 1997 Springer Fachmedien Wiesbaden
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Paulus, D.W.R., Hornegger, J. (1997). Representation of Signals. In: Pattern Recognition of Images and Speech in C++. Vieweg Advanced Studies in Computer Science. Vieweg+Teubner Verlag, Wiesbaden. https://doi.org/10.1007/978-3-663-13991-1_11
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DOI: https://doi.org/10.1007/978-3-663-13991-1_11
Publisher Name: Vieweg+Teubner Verlag, Wiesbaden
Print ISBN: 978-3-528-05558-5
Online ISBN: 978-3-663-13991-1
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