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This book explores the domain of image processing using Python, with the help of working examples and accompanying code. Aimed at researchers and advanced students with a knowledge of image processing fundamentals, this book introduces Python programming via image processing and provides numerous hands-on examples and code snippets. The book will enable readers to appreciate the power of Python in this field, write their own code, and implement complex image processing algorithms such as image enhancement, compression, restoration, segmentation, watermarking, and encryption, and be able to incorporate machine learning models using relevant Python libraries. This book is prepared to meet the needs of young researchers and professionals who are about to start their research journey in the domain of image processing. This book will help readers develop their own applications, whether for software-based implementation or simulation and testing before a final hardware implementation.
Key Features:
- Hands-on Python examples and code for each chapter
- From basics of image processing to advanced topics
- Focus on practical applications such as computer vision
- Includes ML-based applications
Vol 2: Image Processing
Preface
Acknowledgements
Editor biographies
List of contributors
Contributor biographies
1 Basics of image analysis and manipulation using Python
2 Digital image processing using Python language
3 Review and implementation of image segmentation techniques in Python
4 Segmentation of digital images with region growing algorithm
5 Retinal layer segmentation in OCT images
6 Image denoising using wavelet thresholding technique in Python
7 Prostate cancer segmentation of peripheral zone and central gland regions in mpMRI: comparative analysis with deep neural network U-Net and its advanced models
8 Optical character recognition: transforming images into text
9 Automatic COVID-19 identification with a binary neural network using CT images
10 A review and implementation of image despeckling methods
11 Application of image processing and machine learning techniques for vegetation cover classification in precision agriculture