Modern Algorithms for Image Processing: Computer Imagery by Example Using C#
Vladimir Kovalevsky
Résumé
Utilize modern methods for digital image processing and take advantage of the many time-saving templates provided for all of the projects in this book.
Modern Algorithms for Image Processing approaches the topic of image processing through teaching by example. Throughout the book, you will create projects that resolve typical problems that you might encounter in the world of digital image processing. Some projects teach you methods for addressing the quality of images, such as reducing random errors or noise and suppressing pulse noise (salt and pepper), a method valuable for improving the quality of historical images. Other methods detail how to correct inhomogeneous illumination, not by means of subtracting the mean illumination, but through division, a far more efficient method. Additional projects cover contrasting, and a process for edge detection, more efficient than Canny's, for detecting edges in color images directly, without converting them into black and white images.
What You'll Learn
- Apply innovative methods for suppressing pulse noise, enhancing contrast, and edge detection
- Know the pros and cons of enlisting a particular method
- Use new approaches for image compression and recognizing circles in photos
- Utilize a valuable method for straightening photos of paintings taken at an oblique angle, a critical concept to understand when using flash at a right angle
- Understand the problem statement of polygonal approximation of boundaries or edges and its solution
- Use a new method for detecting bicycles in traffic
- Access complete source code examples in C# for all of the projects
Who This Book Is For
C# developers who work with digital image processing or are interested in informatics. The reader should have programming experience and access to an integrated development environment (IDE), ideally .NET.
This book does not prove or disprove theorems, but suggests methods for learning valuable concepts that will enable you to customize your own image processing projects.
Chapter 1: Introduction to Image Processing
Chapter 2: Digital Image Fundamentals
Chapter 3: Image Transforms
Chapter 4: Image Enhancement
Chapter 5: Image Restoration
Chapter 6: Image Compression
Part II: Image Analysis
Chapter 7: Image Segmentation
Chapter 8: Representation and Description
Chapter 9: Recognition and InterpretationChapter 1 describes elements of image processing systems.
Chapter 2 describes the structure of the human eye, basic ideas of sampling and quantization and relationships between pixels. It describes among other the simplest algorithm for labeling connected components in binary images. In Chapter 8 the authors come back to connected components and they describe a method of labeling connected components by means of morphology. But they do not describe a method labeling all components of an image in one go as my "Root Algorithm" described in Section 9.3.
Chapter 3 describes the Fourier transform and some other transforms.
Chapter 4 describes enhancement method in spatial and in frequency domains. The filters, also the median filter, are described very shortly, without mentioning details. This chapter contains a detailed presentation of the fundamentals of color images. The processing of color images is described very shortly with saying that an RGB image should be converted to a HSI image and then its intensity component can be processed with methods developed for gray-level images. My book contains on many places remarks to the difference between processing color and gray-level images.
Chapter 5 describes a very interesting problem of the restoration of degraded images. The authors understand under degradation mostly the blurring of an image (without saying this, which makes the reading difficult). Interesting results of restoring blurred images by means of Wiener filter are presented.
Chapter 6 is devoted to image compression. A statistically based theory of the compression is presented, and different methods of compression are described. However, among the described methods there is no one whose idea is similar to the idea of my method of compression (Section 8 of my book).
Chapter 7 describes different methods of image segmentation but not the segmentation by quantization of colors as described in my Section 9. In this chapter they describe also some primitive methods of edge detection but they do not mention the modern Canny edge detection in spite that the work of Canny was published 7 years before the book was printed.
Chapter 8 covers a primitive procedure for the polygonal approximation of boundaries. This procedure has the drawback that it is impossible to control the precision of the approximation and that it will not correctly work in the case of boundaries having deep indentations. My approximation described in Section 11 is free from these disadvantages.
Chapter 9 describes different methods of recognizing geometric shapes, but no precise methods, such as my described in Sections 12 and 13.
Caractéristiques techniques
| PAPIER | |
| Éditeur(s) | Apress |
| Auteur(s) | Vladimir Kovalevsky |
| Parution | 10/12/2018 |
| Nb. de pages | 274 |
| EAN13 | 9781484242360 |
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