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About this book

This 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 with additional key concepts and methods in image processing. Features: practical examples and carefully constructed chapter-ending exercises; 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 applications; uses ImageJ; 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.

Table of Contents

1. Introduction

Abstract
This third volume in the authors’ Principles of Digital Image Processing series presents a thoughtful selection of advanced topics. Unlike our first two volumes, this one delves deeply into a select set of advanced and largely independent topics. Each of these topics is presented as a separate module which can be understood independently of the other topics, making this volume ideal for readers who expect to work independently and are ready to be exposed to the full complexity (and corresponding level of detail) of advanced, real-world topics.
Wilhelm Burger, Mark J. Burge

2. Automatic Thresholding

Abstract
Although techniques based on binary image regions have been used for a very long time, they still play a major role in many practical image processing applications today because of their simplicity and efficiency. To obtain a binary image, the first and perhaps most critical step is to convert the initial grayscale (or color) image to a binary image, in most cases by performing some form of thresholding operation, as described in Volume 1, Section 4.1.4 [20].
Wilhelm Burger, Mark J. Burge

3. Filters for Color Images

Abstract
Color images are everywhere and filtering them is such a common task that it does not seem to require much attention at all. In this chapter, we describe how classical linear and non-linear filters, which we covered before in the context of grayscale images, can be either used directly or adapted for the processing of color images. Often color images are treated as stacks of intensity images and existing monochromatic filters are simply applied independently to the individual color channels. While this is straightforward and performs satisfactorily in many situations, it does not take into account the vector-valued nature of color pixels as samples taken in a specific, multi-dimensional color space. As we show in this chapter, the outcome of filter operations depends strongly on the working color space and the variations between different color spaces may be substantial. Although this may not be apparent in many situations, it should be of concern if high-quality color imaging is an issue.
Wilhelm Burger, Mark J. Burge

4. Edge Detection in Color Images

Abstract
Edge information is essential in many image analysis and computer vision applications and thus the ability to locate and characterize edges robustly and accurately is an important task. Basic techniques for edge detection in grayscale images are discussed in Chapter 6 of Volume 1 [20]. Color images contain richer information than grayscale images and it appears natural to assume that edge detection methods based on color should outperform their monochromatic counterparts. For example, locating an edge between two image regions of different hue but similar brightness is difficult with an edge detector that only looks for changes in image intensity. In this chapter, we first look at the use of “ordinary” (i. e., monochromatic) edge detectors for color images and then discuss dedicated detectors that are specifically designed for color images.
Wilhelm Burger, Mark J. Burge

5. Edge-Preserving Smoothing Filters

Abstract
Noise reduction in images is a common objective in image processing, not only for producing pleasing results for human viewing but also to facilitate easier extraction of meaningful information in subsequent steps, for example, in segmentation or feature detection. Simple smoothing filters, such as the Gaussian filter and the filters discussed in Chapter 3 of this volume effectively perform low-pass filtering and thus remove high-frequency noise. However, they also tend to suppress high-rate intensity variations that are part of the original signal, thereby destroying image structures that are visually important. The filters described in this chapter are “edge preserving” in the sense that they change their smoothing behavior adaptively depending upon the local image structure. In general, maximum smoothing is performed over “flat” (uniform) image regions, while smoothing is reduced near or across edge-like structures, typically characterized by high intensity gradients.
Wilhelm Burger, Mark J. Burge

6. Fourier Shape Descriptors

Abstract
Fourier descriptors are an interesting method for modeling 2D shapes that are described as closed contours. Unlike polylines or splines, which are explicit and local descriptions of the contour, Fourier descriptors are global shape representations, i. e., each component stands for a particular characteristic of the entire shape. If one component is changed, the whole shape will change. The advantage is that it is possible to capture coarse shape properties with only a few numeric values, and the level of detail can be increased (or decreased) by adding (or removing) descriptor elements. In the following, we describe what is called “cartesian” (or “elliptical”) Fourier descriptors, how they can be used to model the shape of closed 2D contours and how they can be adapted to compare shapes in a translation-, scale- and rotation-invariant fashion.
Wilhelm Burger, Mark J. Burge

7. SIFT—Scale-Invariant Local Features

Abstract
Many real applications require the localization of reference positions in one or more images, for example, for image alignment, removing distortions, object tracking, 3D reconstruction etc. We have seen that corner points can be located quite reliably and independent to orientation. However, typical corner detectors only provide the position and strength of each candidate point but do not provide any information about its characteristic or “identity” that could be used for matching. Another limitation is that most corner detectors only operate at a particular scale or resolution, since they are based on a rigid set of filters.
Wilhelm Burger, Mark J. Burge
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