By Wilhelm Burger
ISBN-10: 1848829183
ISBN-13: 9781848829183
ISBN-10: 1848829191
ISBN-13: 9781848829190
This textbook is the 3rd of 3 volumes which offer a latest, algorithmic creation to electronic snapshot processing, designed for use either via beginners wanting a company beginning on which to construct, and practitioners looking for severe research and urban implementations of an important suggestions. This quantity builds upon the introductory fabric awarded within the first volumes with extra key innovations and techniques in picture processing. good points: useful examples and punctiliously developed chapter-ending workouts; genuine implementations, concise mathematical notation, and distinctive algorithmic descriptions designed for programmers and practitioners; simply adaptable Java code and fully worked-out examples for simple inclusion in latest functions; makes use of ImageJ; presents a supplementary web site with the whole Java resource code, try photos, and corrections; extra presentation instruments for teachers together with an entire set of figures, tables, and mathematical elements.
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Additional info for Principles of Digital Image Processing: Advanced Methods
Example text
When computing the local average and variance, attention must be paid to the situation at the image borders, as illustrated in Fig. 15. Two approaches are frequently used. In the first approach (following the common practice for implementing filter operations), all outside pixel values are replaced by the closest inside pixel, which is always a border pixel. Thus the border pixel values are effectively replicated outside the image boundaries and thus these pixels have a strong influence on the local results.
55) 30 2. 7 Results from minimum-error thresholding. Calculated threshold values q and resulting binary images (a–d). The green and blue graphs in (e–h) show the fitted Gaussian background and foreground distributions N0 = (μ0 , σ0 ) and N1 = (μ1 , σ1 ), respectively. The red graph corresponds to the error quantity e(q) for varying threshold values q = 0, . . , 255 (see Eqn. 52)). The optimal threshold qmin is located at the minimum of e(q) (dashed vertical line). The estimated parameters of the background/foreground Gaussians are listed at the bottom.
5 Maximum entropy thresholding Entropy is an important concept in information theory and particularly in data compression [51,53]. It is a statistical measure that quantifies the average amount of information contained in the “messages” generated by a stochastic data source. For example, the N pixels in an image I can be interpreted as a message of N symbols, each taken independently from a finite alphabet of K (e. , 256) different intensity values. Knowing the probability of each intensity value g to occur, entropy measures how likely it is to observe a particular image, or, in other words, how much we should be surprised to see such an image.