A short summary of this paper. Gonzalez University of Tennessee Richard E. Eddins The MathWorks, Inc. No part of this book may be reproduced or transmitted in any form or by any means, without written permission from the publisher.
These efforts include the development, research, and testing of the theories and programs to determine their effectiveness. The authors and publisher shall not be liable in any event for incidental or consequential damages with, or arising out of, the furnishing, performance, or use of these programs. These functions, and the expressiveness of the MATLAB language, make image-processing operations easy to write in a compact, clear manner, thus providing an ideal software prototyping environment for the solution of image processing problems.
In this chapter we introduce the basics of MATLAB notation, discuss a number of fundamental toolbox properties and functions, and begin a discussion of programming concepts. Thus, the material in this chapter is the foundation for most of the software-related discussions in the remainder of the book. The term gray level is used often to refer to the intensity of monochrome images. Color images are formed by a combination of individual images. For example, in the RGB color system a color image consists of three individual monochrome images, referred to as the red R , green G , and blue B primary or component images.
For this reason, many of the techniques developed for monochrome images can be ex- tended to color images by processing the three component images individually. Color image processing is the topic of Chapter 7. Converting such an image to digital form requires that the coordinates, as well as the amplitude, be digitized. Digitizing the coordinate values is called sampling; digitizing the amplitude values is called quantization.
Thus, when x, y, and the amplitude val- ues of f are all finite, discrete quantities, we call the image a digital image. We use two principal ways in this book to represent digital images. Assume that an image f x, y is sampled so that the resulting image has M rows and N columns. The values of the coordinates are discrete quantities. For notational clarity and convenience, we use integer values for these discrete coordinates.
The notation 0, 1 is used to signify the second sample along the first row. It does not mean that these are the actual values of physical coordinates when the image was sampled.
Figure 2. Note that x ranges from 0 to M - 1 and y from 0 to N - 1 in integer increments. The coordinate convention used in the Image Processing Toolbox to denote arrays is different from the preceding paragraph in two minor ways.
First, in- stead of using x, y , the toolbox uses the notation r, c to indicate rows and columns. Note, however, that the order of coordinates is the same as the order discussed in the previous paragraph, in the sense that the first element of a coordinate tuple, a, b , refers to a row and the second to a column. Image Processing Toolbox documentation refers to the coordinates in Fig. Less frequently, the toolbox also employs another coordinate convention, called spatial coordinates, that uses x to refer to columns and y to refers to rows.
This is the opposite of our use of variables x and y. Each element of this array is called an image element, picture element, pixel, or pel. The terms image and pixel are used throughout the rest of our discussions to denote a digital image and its elements.
Clearly, the two representations are identical, except for the shift in origin. Typically, we use the letters M and N, respectively, to denote the number of rows and columns in a matrix. Variables must begin with a letter and contain only letters, numerals, and underscores. We use conventional Roman, italic notation, such as f x, y , for mathematical ex- pressions. Recall from Section 1. Note the semicolon ; use of single quotes ' to delimit the string filename. When, as in the preceding command line, no path information is included In Windows, directories in filename, imread reads the file from the Current Directory and, if that are called folders.
The simplest way to read an image from a specified directory is to include a full or relative path to that directory in filename. Table 2. PBM Portable Bitmap. That is, this command gives the number of rows in f. The second dimension of an array is in the horizontal direction, so the state- ment size f, 2 gives the number of columns in f. The whos function displays additional information about an array. A semicolon at the end of a whos line has no effect, so normally one is not used.
Using the syntax number of other syntax forms for performing tasks such as controlling imshow f, [low high] image magnification. Consult the help page for imshow for additional displays as black all values less than or equal to low, and as white all values details. The values in between are displayed as interme- diate intensity values. Finally, the syntax imshow f, [ ] sets variable low to the minimum value of array f and high to its maximum value. This form of imshow is useful for displaying images that have a low dynamic range or that have positive and negative values.
Note the figure number on the top, left of the window. In most of the examples throughout the book, only the images themselves are shown. Note the various pull-down menus and utility buttons. They are used for processes such as scaling, saving, and exporting the contents of the display window. In particular, the Edit menu has functions for editing and formatting the contents before they are printed or saved to disk.
Note that more than one command can be written on a figure window. Typing line, provided that different commands are delimited by commas or semico- figure n forces figure number n to become lons. As mentioned earlier, a semicolon is used whenever it is desired to sup- visible. Finally, suppose that we have just read an image, h, and find that using imshow h produces the image in Fig.
The improvement is apparent. To start the Image Tool, use the imtool function. Original image courtesy of Dr. David R. Pickens, Vanderbilt University Medical Center. The large, central window is the main view. The Measure Distance tool is in use, showing that the distance between the two pixels enclosed by the small boxes is The Overview Window, on the left side of Fig.
The Main Window view can be adjusted by dragging the rectangle in the Overview Window. The Pixel Region Window shows individual pixels from the small square region on the upper right tip of the rose, zoomed large enough to see the actual pixel values. In addition to the these tools, the Main and Overview Win- dow toolbars provide controls for tasks such as image zooming, panning, and scrolling. Pixel Region Superimposes pixel values on a zoomed-in pixel view.
Distance Measures the distance between two pixels. Image Information Displays information about images and image files. Adjust Contrast Adjusts the contrast of the displayed image. Crop Image Defines a crop region and crops the image. Display Range Shows the display range of the image data. Overview Shows the currently visible image. Alternatively, the desired format can be specified explicitly with a third in- put argument.
This syntax is useful when the desired file does not use one of the recognized file extensions. For example, the following command writes f to a TIFF file called patient A more general imwrite syntax applicable only to JPEG images is imwrite f, 'filename.
In order to function imfinfo. See Example 2. The book has been organized into two parts. Part I: Image Processing begins with an overview of the field, then introduces the fundamental concepts, notation, and terminology associated with image representation and basic image processing operations.
These chapters cover image acquisition and digitization; arithmetic, logic, and geometric operations; point-based, histogram-based, and neighborhood-based image enhancement techniques; the Fourier Transform and relevant frequency-domain image filtering techniques; image restoration; mathematical morphology; edge detection techniques; image segmentation; image compression and coding; and feature extraction and representation.
Part II: Video Processing presents the main concepts and terminology associated with analog video signals and systems, as well as digital video formats and standards. The approach taken is essentially practical and the book offers a framework within which the concepts can be understood by a series of well chosen examples, exercises and computer experiments, drawing on specific examples from within science, medicine and engineering.
Clearly divided into eleven distinct chapters, the book begins with a fast-start introduction to image processing to enhance the accessibility of later topics. Subsequent chapters offer increasingly advanced discussion of topics involving more challenging concepts, with the final chapter looking at the application of automated image classification with Matlab examples.
Matlab is frequently used in the book as a tool for demonstrations, conducting experiments and for solving problems, as it is both ideally suited to this role and is widely available.
Prior experience of Matlab is not required and those without access to Matlab can still benefit from the independent presentation of topics and numerous examples. Features a companion website www. Includes numerous examples, graded exercises and computer experiments to support both students and instructors alike.
It describes classical as well emerging areas in image processing and analysis. This valuable guide provides tools for quantifying the ice environment that needs to be identified and reproduced for such testing. This includes fit-for-purpose studies of existing vessels, new-build conceptual design and detailed engineering design studies for new developments, and studies of demanding marine operations involving multiple vessels and operational scenarios in sea ice.
A major contribution of this work is the development of automated computer algorithms for efficient image analysis.
These are used to process individual sea-ice images and video streams of images to extract parameters such as ice floe size distribution, and ice types. Readers are supplied with Matlab source codes of the algorithms for the image processing methods discussed in the book made available as online material. The book covers topics that can be introduced with simple mathematics so students can learn the concepts without getting overwhelmed by mathematical detail.
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