A Beginner’s Guide to Image Preprocessing Techniques: 1st Edition (Hardback) book cover

A Beginner’s Guide to Image Preprocessing Techniques

1st Edition

By Jyotismita Chaki, Nilanjan Dey

CRC Press

100 pages | 123 B/W Illus.

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pub: 2018-11-05
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For optimal computer vision outcomes, attention to image pre-processing is required so that one can improve image features by eliminating unwanted falsification. This book emphasizes various image pre-processing methods which are necessary for early extraction of features from the image. Effective use of image pre-processing can offer advantages and resolve complications that finally results in improved detection of local and global features. Different approaches for image enrichments and improvements are conferred in this book that will affect the feature analysis depending on how the procedures are employed.

Key Features

  • Describes the methods used to prepare images for further analysis which includes noise removal, enhancement, segmentation, local, and global feature description
  • Includes image data pre-processing for neural networks and deep learning
  • Covers geometric, pixel brightness, filtering, mathematical morphology transformation, and segmentation pre-processing techniques
  • Illustrates a combination of basic and advanced pre-processing techniques essential to computer vision pipeline
  • Details complications to resolve using image pre-processing

Table of Contents

Chapter 1: Perspective of Image Preprocessing on Image Processing

1.1 Introduction to Image Preprocessing

1.2 Complications to resolve using Image Preprocessing

1.3 Effect of Image Preprocessing on Image Recognition

1.4 Summary

1.5 References

Chapter 2: Pixel Brightness Transformation Techniques

2.1 Position Dependent Brightness Correction

2.2 Grayscale Transformations

2.3 Summary

2.4 References

Chapter 3: Geometric Transformation Techniques

3.1 Pixel Coordinate Transformation or Spatial Transformation

3.2 Brightness Interpolation

3.3 Summary

3.4 References

Chapter 4: Filtering Techniques

4.1 Spatial filter

4.2 Frequency Filter

4.3 Summary

4.4 References


Chapter 5: Segmentation Techniques

5.1 Thresholding

5.2 Edge Based Segmentation

5.3 Region-Based Segmentation

5.4 Summary

5.5 References

Chapter 6: Mathematical Morphology Techniques

6.1 Binary Morphology

6.2 Grayscale Morphology

6.3 Summary

6.4 References

Chapter 7: Other Applications of Image Preprocessing

7.1 Preprocessing of Color Images

7.2 Image preprocessing for Neural Networks and Deep learning

7.3 Summary

7.4 References

About the Authors

Jyotismita Chaki, PhD. has done her PhD (Engg) from Jadavpur University, School of Education Technology Department, Kolkata, India. Her research interests include: Computer Vision and Image Processing, Pattern Recognition, Medical Imaging, Soft computing, Data mining, Machine learning. She has published 14 international conferences and journal papers. She has also served as a Program Committee member of 2nd International Conference on Advanced Computing and Intelligent Engineering 2017 (ICACIE-2017), 4TH International Conference on Image Information Processing (ICIIP-2017).

Nilanjan Dey, PhD. is currently associated with Department of Information Technology, TechnoIndia College of Technology, Kolkata, W.B., India. He holds an honorary position of Visiting Scientistat Global Biomedical Technologies Inc., CA, USA and Research Scientist of Laboratory of AppliedMathematical Modeling in Human Physiology, Territorial Organization of- Scientific and EngineeringUnions, BULGARIA. He is an Associate Researcher of Laboratoire RIADI, University of Manouba, TUNISIA. He is the Associated Member of Wearable Computing Research lab, University of Reading,London, UK.

His research topic is Medical Imaging, Soft computing, Data mining, Machine learning, Rough sets,Computer Aided Diagnosis, Atherosclerosis. He has 25 books and 300 international conferences andjournal papers. He is the Editor-in-Chief of International Journal of Ambient Computing andIntelligence (IGI Global), US (Scopus, ESCI, ACM dl and DBLP listed), International Journal of RoughSets and Data Analysis (IGI Global), US. Co-Editor-in-chief of International Journal of SyntheticEmotions (IJSE), IGI Global, US, and International Journal of Natural Computing Research (IGI Global), US. Series Editor of Advances in Geospatial Technologies (AGT) Book Series, (IGI Global), US, SeriesEditor of Advances in Ubiquitous Sensing Applications for Healthcare (AUSAH), Elsevier. Executive Editor of International Journal of Image Mining (IJIM), Inderscience, and Associated Editor of IEEE Access journal and the International Journal of Service Science, Management, Engineeringand Technology, IGI Global. He is a life member of IE, UACEE, ISOC. He is associated with manyInternational Conferences as a Chair (ITITS 2017-China, WS4 2017-London, INDIA 2017-Vietnam etc.).

About the Series

Intelligent Signal Processing and Data Analysis

In the current era of huge amount of data types and measurement in all sectors and applications, the same requires automated capturing, data analysis and evaluation methods. Consequently, sophisticated intelligent approaches become essential as flexible and powerful tools based on different signal processing algorithms for multiple applications. Intelligent signal processing (ISP) methods are progressively swapping the conventional analog signal processing techniques in several domains, such as speech analysis and processing, biomedical signal analysis radar and sonar signal processing, and processing, telecommunications, and geophysical signal processing. The main focus is this book series is to find out the new trends and techniques in the intelligent signal processing and data analysis leading to scientific breakthroughs in applied applications. Artificial fuzzy logic, deep learning, optimization algorithms, and neural networks are presented for signal processing applications. The series main emphasis is to offer both extensiveness diversity of selected intelligent signal processing and depth in the selected state-of-the-art data analysis techniques for solving real world problems.

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Subject Categories

BISAC Subject Codes/Headings:
COMPUTERS / Machine Theory