Download E-books Principles of Digital Image Processing, Volume 3: Advanced Methods (Undergraduate Topics in Computer Science) PDF

By Wilhelm Burger, Mark J. Burge

This easy-to-follow textbook is the 3rd of 3 volumes which supply a contemporary, algorithmic creation to electronic snapshot processing, designed for use either through freshmen wanting an organization beginning on which to construct, and practitioners looking for serious research and urban implementations of crucial recommendations. This quantity builds upon the introductory fabric provided within the first volumes (Fundamental innovations and middle Algorithms) with extra key options and strategies in snapshot processing.

Features and topics:
* useful examples and thoroughly developed chapter-ending routines drawn from the authors' years of expertise educating this material
* genuine implementations, concise mathematical notation, and unique algorithmic descriptions designed for programmers and practitioners
* simply adaptable Java code and fully worked-out examples for simple inclusion in present (and swift prototyping of latest) applications
* makes use of ImageJ, the picture processing method built, maintained, and freely dispensed through the U.S. nationwide Institutes of overall healthiness (NIH)
* presents a supplementary web site with the full Java resource code, try out pictures, and corrections—www.imagingbook.com
* extra 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 knowing of the subject and may be valuable for extra constructing wisdom through self-study.

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214 6. 6. 1 Magnitude-only matching . . . . . . . . . . . . . . . . . . . . . . . . . . . 214 6. 6. 2 advanced (phase-preserving) matching . . . . . . . . . . . . . . . . . 218 6. 7 Java implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219 6. eight precis and extra interpreting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 225 6. nine routines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 225 7. SIFT—Scale-Invariant neighborhood positive factors . . . . . . . . . . . . . . . . . . . . . . . 229 7. 1 curiosity issues at a number of scales . . . . . . . . . . . . . . . . . . . . . . . . . . . 230 7. 1. 1 The Laplacian-of-Gaussian (LoG) clear out . . . . . . . . . . . . . . . . 231 7. 1. 2 Gaussian scale area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 237 xii ideas of electronic picture Processing • complicated tools 7. 2 7. three 7. four 7. five 7. 6 7. 7 7. eight 7. 1. three LoG/DoG scale house . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 240 7. 1. four Hierarchical scale area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 242 7. 1. five Scale house implementation in SIFT . . . . . . . . . . . . . . . . . . 248 Key element choice and refinement . . . . . . . . . . . . . . . . . . . . . . . . . 252 7. 2. 1 neighborhood extrema detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 255 7. 2. 2 place refinement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 257 7. 2. three Suppressing responses to edge-like buildings . . . . . . . . . . . 260 developing neighborhood Descriptors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 263 7. three. 1 discovering dominant orientations . . . . . . . . . . . . . . . . . . . . . . . 263 7. three. 2 Descriptor formation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 267 SIFT set of rules precis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 276 Matching SIFT good points . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 276 7. five. 1 function distance and fit caliber . . . . . . . . . . . . . . . . . . . 285 7. five. 2 Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 287 Efficient characteristic matching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 289 SIFT implementation in Java . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 294 7. 7. 1 SIFT function extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 294 7. 7. 2 SIFT characteristic matching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 295 workouts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 296 Appendix A. Mathematical Symbols and Notation . . . . . . . . . . . . . . . . . . . . . . . 299 B. Vector Algebra and Calculus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 305 B. 1 Vectors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 305 B. 1. 1 Column and row vectors . . . . . . . . . . . . . . . . . . . . . . . . . . . . 306 B. 1. 2 Vector size . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 306 B. 2 Matrix multiplication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 306 B. 2. 1 Scalar multiplication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 306 B. 2. 2 made from matrices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 307 B. 2. three Matrix-vector items . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 307 B. three Vector items . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 308 B. three. 1 Dot product . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 308 B. three. 2 Outer product . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 309 B. four Eigenvectors and eigenvalues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 309 B. four. 1 Eigenvectors of a 2 × 2 matrix . . . . . . . . . . . . . . . . . . . . . . . 310 B. five Parabolic becoming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 311 B. five. 1 becoming a parabolic functionality to 3 pattern issues . . . . . 311 B. five. 2 Parabolic interpolation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 313 B. 6 Vector fields .

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