Principles of Digital Image Processing, Volume 3: Advanced Methods (Undergraduate Topics in Computer Science)

Principles of Digital Image Processing, Volume 3: Advanced Methods (Undergraduate Topics in Computer Science)

Wilhelm Burger, Mark J. Burge

Language: English

Pages: 374

ISBN: 2:00260318

Format: PDF / Kindle (mobi) / ePub

This easy-to-follow textbook is the third of three volumes which provide a modern, algorithmic introduction to digital image processing, designed to be used both by learners desiring a firm foundation on which to build, and practitioners in search of critical analysis and concrete implementations of the most important techniques. This volume builds upon the introductory material presented in the first two volumes (Fundamental Techniques and Core Algorithms) with additional key concepts and methods in image processing.

Features and topics:
* Practical examples and carefully constructed chapter-ending exercises drawn from the authors' years of experience teaching this material
* Real implementations, concise mathematical notation, and precise algorithmic descriptions designed for programmers and practitioners
* Easily adaptable Java code and completely worked-out examples for easy inclusion in existing (and rapid prototyping of new) applications
* Uses ImageJ, the image processing system developed, maintained, and freely distributed by the U.S. National Institutes of Health (NIH)
* Provides a supplementary website with the complete Java source code, test images, and corrections—
* Additional presentation tools for instructors including a complete set of figures, tables, and mathematical elements

This thorough, reader-friendly text will equip undergraduates with a deeper understanding of the topic and will be invaluable for further developing knowledge via self-study.

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Algorithm Design: Solutions Manual















(2.31) i=0 Thus, the entropy of a discrete source with an alphabet of K different symbols is always in the range [0, log(K)]. Using image entropy for threshold selection The use of image entropy as a criterion for threshold selection has a long tradition and numerous methods have been proposed. In the following, we describe the early but still popular technique by Kapur et al. [52,64] as a representative example. Given a particular threshold q (with 0 ≤ q < K −1), the estimated probability.

Thr = new BernsenThresholder(); ByteProcessor Q = thr.getThreshold(bp); thr.threshold(bp, Q); ... Alternatively, the same operation can be defined without making Q explicit as 6 7 8 9 ... AdaptiveThresholder thr = new BernsenThresholder(); thr.threshold(bp); ... Available real sub-classes of AdaptiveThresholder that can be instantiated (as in line 2 above) are BernsenThresholder (class) Implements Bernsen’s adaptive thresholding method, see Section 2.2.1 and Alg. 2.7 (on p. 32).

(5.72) βmax ← −∞ for k ← 1, . . . , K and all coordinates (u, v) ∈ M ×N do B(k, u, v) ← trace(A(u, v)·H(k, u, v)) ◃ βk , Eqn. (5.74) βmax ← max(βmax , |B(k, u, v)|) α ← dt /βmax ◃ Eqn. (5.75) for k ← 1, . . . , K and all coordinates (u, v) ∈ M ×N do Ik (u, v) ← Ik (u, v) + α · B(k, u, v) ◃ update the image return I. 5.4 Measuring image quality 163 (a) a1 = 0.50 (b) a1 = 0.25 (c) a1 = 0.00 Figure 5.20 Tschumperlé-Deriche filter example. The non-isotropy of the filter can be adjusted by.

Gaussian kernel Hr (x) = √2π·σ 2 ). Investigate the effects of this filter for σ = 10, 20, and 25 upon the image and its histogram. Exercise 5.2 Create an ImageJ plugin (or other suitable implementation) to produce a Gaussian noise image with mean value m and standard deviation s, based on the description in Section C.3.3 of the Appendix. Inspect the histogram of the resulting image to see if the distribution of pixel values is Gaussian. Calculate the mean (µ) and the variance (σ 2 ) of the.

Multiplying ei 2πt by a complex factor z stretches the radius of the circle by |z|, and also changes the phase (starting angle) of the circle by an angle θ, that is, z · eiϕ = |z| · ei(ϕ+θ), with θ = z = arg(z) = tan−1 (Im(z)/ Re(z)). (6.27) 182 6. Fourier Shape Descriptors G−j G−1 G−2 G0 G1 G2 Gj G Im (1) g2 (1) g1 (1) g 0 = G1 r1 (1) gM−1 θ1 Re (0, 0) r1 = |G1 | θ1 = tan−1 Im(G1 ) Re(G1 ) Figure 6.6 A single DFT coefficient corresponds to a circle. The partial.

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