Digital Image Processing
6th Edition
Published on 28. September 2005
XIV, 608 pages
978-3-540-27563-3 (ISBN)
Description
The sixth edition of this worldwide used textbook was thoroughly - vised and extended. Throughout the whole text you will ?nd numerous improvements, extensions, and updates. Above all, I would like to draw your attention to two major changes. Firstly, the whole textbook is now clearly partitioned into basic and advanced material in order to cope with the ever-increasing ?eld of di- talimageprocessing. Themostimportantequationsareputintoframed boxes. The advanced sections are located in the second part of each chapter and are marked by italic headlines and by a smaller typeface. In this way, you can ?rst work your way through the basic principles of digital image processing without getting overwhelmed by the wealth of the material. You can extend your studies later to selected topics of interest. The second most notable extension are exercises that are now - cluded at the end of each chapter. These exercise help you to test your understanding, train your skills, and introduce you to real-world image processing tasks. The exercises are marked with one to three stars to indicate their di?culty. An important part of the exercises is a wealth of interactive computer exercises, which cover all topics of this te- book. These exercises are performed with the image processing so- ware heurisko® (http://www. heurisko. de), which is included on the accompanying CD-ROM. In this way you can get own practical expe- ence with almost all topics and algorithms covered by this book.
More details
Series
Edition
Sixth Edition 2005
Language
English
Place of publication
Berlin
Germany
Publishing group
Springer Berlin
Target group
Professional and scholarly
Edition type
Revised edition
Illustrations
XIV, 608 p. 248 illus.
File size
20,82 MB
ISBN-13
978-3-540-27563-3 (9783540275633)
DOI
10.1007/3-540-27563-0
Schweitzer Classification
Other editions
Additional editions
Book
04/2005
6th Edition
Springer
€149.79
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Content
Foundation.- Applications and Tools.- Image Representation.- Random Variables and Fields.- Neighborhood Operations.- Multiscale Representation.- Image Formation and Preprocessing.- Quantitative Visualization.- Image Formation.- 3-D Imaging.- Digitization, Sampling, Quantization.- Pixel Processing.- Feature Extraction.- Averaging.- Edges.- Simple Neighborhoods.- Motion.- Texture.- Image Analysis.- Segmentation.- Regularization and Modeling.- Morphology.- Shape Presentation and Analysis.- Classification.
14 Motion (p. 397-398)
14.1 Introduction
Motion analysis long used to be a specialized research area that had not much to do with general image processing. This separation had two reasons. First, the techniques used to analyze motion in image sequences were quite different. Second, the large amount of storage space and computing power required to process image sequences made image sequence analysis available only to a few specialized institutions that could afford to buy the expensive specialized equipment.
Both reasons are no longer true. Because of the general progress in image processing, the more advanced methods used in motion analysis no longer differ from those used for other image processing tasks. The rapid progress in computer hardware and algorithms makes the analysis of image sequences now feasible even on standard personal computers and workstations.
Therefore we treat motion in this chapter as just another feature that can be used to identify, characterize, and distinguish objects and to understand scenes. Motion is indeed a powerful feature. We may compare the integration of motion analysis into mainstream image processing with the transition from still photography to motion pictures.
Only image sequence analysis allows us to recognize and analyze dynamic processes. Thus far-reaching capabilities become available for scientific and engineering applications including the study of flow; transport; biological growth processes from the molecular to the ecosystem level; diurnal, annual, and interannual variations; industrial processes; trafic; autonomous vehicles and robots - to name just a few application areas. In short, everything that causes temporal changes or makes them visible in our world is a potential subject for image sequence analysis.
The analysis of motion is still a challenging task and requires some special knowledge. Therefore we discuss the basic problems and principles of motion analysis in Section 14.2. Then we turn to the various techniques for motion determination. As in many other areas of image processing, the literature is swamped with a multitude of approaches. This book should not add to the confusion. We emphasize instead the basic principles and we try to present the various concepts in a unified way as filter operations on the space-time images.
In this way, the interrelations between the different concepts are made transparent. In this sense, we will discuss differential (Section 14.3), tensor (Section 14.4), correlation (Section 14.5), and phase (Section 14.6) techniques as elementary motion estimators.
14.1 Introduction
Motion analysis long used to be a specialized research area that had not much to do with general image processing. This separation had two reasons. First, the techniques used to analyze motion in image sequences were quite different. Second, the large amount of storage space and computing power required to process image sequences made image sequence analysis available only to a few specialized institutions that could afford to buy the expensive specialized equipment.
Both reasons are no longer true. Because of the general progress in image processing, the more advanced methods used in motion analysis no longer differ from those used for other image processing tasks. The rapid progress in computer hardware and algorithms makes the analysis of image sequences now feasible even on standard personal computers and workstations.
Therefore we treat motion in this chapter as just another feature that can be used to identify, characterize, and distinguish objects and to understand scenes. Motion is indeed a powerful feature. We may compare the integration of motion analysis into mainstream image processing with the transition from still photography to motion pictures.
Only image sequence analysis allows us to recognize and analyze dynamic processes. Thus far-reaching capabilities become available for scientific and engineering applications including the study of flow; transport; biological growth processes from the molecular to the ecosystem level; diurnal, annual, and interannual variations; industrial processes; trafic; autonomous vehicles and robots - to name just a few application areas. In short, everything that causes temporal changes or makes them visible in our world is a potential subject for image sequence analysis.
The analysis of motion is still a challenging task and requires some special knowledge. Therefore we discuss the basic problems and principles of motion analysis in Section 14.2. Then we turn to the various techniques for motion determination. As in many other areas of image processing, the literature is swamped with a multitude of approaches. This book should not add to the confusion. We emphasize instead the basic principles and we try to present the various concepts in a unified way as filter operations on the space-time images.
In this way, the interrelations between the different concepts are made transparent. In this sense, we will discuss differential (Section 14.3), tensor (Section 14.4), correlation (Section 14.5), and phase (Section 14.6) techniques as elementary motion estimators.
