By Wilhelm Burger
ISBN-10: 1848001940
ISBN-13: 9781848001947
This easy-to-follow textbook is the second one of 3 volumes which offer a contemporary, algorithmic advent to electronic snapshot processing, designed for use either via inexperienced persons needing an organization beginning on which to construct, and practitioners looking for serious research and urban implementations of crucial ideas. This quantity extends the introductory fabric awarded within the first quantity (Fundamental Techniques) with extra recommendations that shape a part of the normal image-processing toolbox.
Features and topics:
- Practical examples and thoroughly developed chapter-ending workouts drawn from the authors' years of expertise educating this material
- Real implementations, concise mathematical notation, and detailed algorithmic descriptions designed for programmers and practitioners
- Easily adaptable Java code and entirely worked-out examples for simple inclusion in present (and swift prototyping of recent) functions
- Uses ImageJ, the picture processing approach constructed, maintained, and freely allotted via the U.S. nationwide Institutes of overall healthiness (NIH)
- Provides a supplementary web site with the entire Java resource code, try photos, and corrections – www.imagingbook.com
- Additional presentation instruments for teachers together with a whole set of figures, tables, and mathematical elements
This thorough, reader-friendly textual content will equip undergraduates with a deeper realizing of the subject and may be beneficial for additional constructing wisdom through self-study.
Wilhelm Burger, Ph.D., is the director of the electronic Media measure courses on the top Austria collage of technologies at Hagenberg.
Mark J. Burge, Ph.D., is a senior crucial within the middle for nationwide defense and Intelligence at Noblis in Washington, D.C.
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Extra resources for Principles of Digital Image Processing: Core Algorithms
Sample text
In the other case the remaining contour points are found by repeatedly calling findNextPoint(), and for every successive pair of points the current point xc (xC, yC) and the previous point xp (xP, yP) are recorded. Only when both points correspond to the original starting 4 In the following description the names in parentheses after the algorithmic symbols denote the corresponding identifiers used in the Java implementation. 5 Combined contour tracing and region labeling (Part 1). Given a binary image I, the method CombinedContourLabeling() returns a set of contours and an array containing region labels for all pixels in the image.
When using a differential chain code, the situation improves slightly. Instead of encoding the difference in the position of the next contour point, the change in the direction along the discrete contour is encoded. A given absolute chain code cR = [c0 , c1 , . . cM−1 ] can be converted element by element to a differential chain code cR = [c0 , c1 , . . 7 The element ci thus describes the change in direction (curvature) of the contour between two successive segments ci and ci+1 of the original chain code cR .
19 Orientation and eccentricity examples. The orientation θ (Eqn. 25)) is displayed for each connected region as a vector with the length proportional to the region’s eccentricity value Ecc(R) (Eqn. 31)). Also shown are the ellipses (Eqns. 33)) corresponding to the orientation and eccentricity parameters. respectively, with a1 , a2 as defined in Eqn. 31) and |R| being the number of pixels in the region. 34) for 0 ≤ t < 2π. If entirely filled, the region described by this ellipse would have the same (first and second order) central moments as the original region R.