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This chapter introduces a number of algorithms that are characterized by changing the value of each pixel in an image completely independent on the values of the surrounding pixels. The key concept in this regards is gray-level mapping. First linear gray-level mapping is discussed and exemplified by changing the brightness and contrast in images. Next non-linear gray-level mapping is covered. Hereafter the chapter turns to one of the fundamental concepts within image processing, namely the image histogram. This is then combined with gray-level resulting in methods for improving the image quality and methods for binarising the image by the use of a threshold approach. Due to the importance of the thresholding approach it is discussed in details and different approaches are presented. The chapter finally presents how to combine two images through logic or arithmetic.
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In practice the line is not rotated around (0,0) but rather around the center point (127,127), hence b=127(1− a). However, for the discussion here it suffice to say that b=0 and only look at the slope.
How to calculate the average is discussed in the next chapter.
In the subtraction process both positive and negative values can appear. Since we are only interested in the difference we take the absolute value.
Note that the above order of scanning through the image and the code example is general and used for virtually all methods, operations and algorithms presented in this book.
go back to reference Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979) MathSciNetCrossRef Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979) MathSciNetCrossRef
- Point Processing
Thomas B. Moeslund
- Springer London
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