Image Processing with Python: A practical approach

✍ Scribed by Irshad Ahmad Ansari (editor), Varun Bajaj (editor)

• Publisher: Institute of Physics Publishing
• Year: 2024
• Tongue: English
• Leaves: 300
• Category: Library

✦ Synopsis

This book explores the domain of image processing using Python, with the help of working examples and accompanying code. The example-led implementation of Python codes is provided in this book to train budding researchers, coders, and hobbyists in the field of machine intelligence and image processing.

✦ Table of Contents

PRELIMS.pdf
Acknowledgements
Editor biographies
Irshad Ahmad Ansari
Varun Bajaj
List of contributors
Contributor biographies
Outline placeholder
Om Asati
Venkata Siva Prasad Bhagavatula
Siddharth Bhalerao
Devanand Bhonsle
V R Deepthi
Akanksha Dixit
Anil B Gavade
Priyanka A Gavade
Zahra Ghanbari
Swati Hadke
Atiya Khan
Nikhil Kushwaha
Rajveer Singh Lalawat
Shankar Mali
Ravi Mishra
Anupama Mohabansi
Sheetal Mungale
Manjushree Nayak
Rajendra B Nerli
Chandrashekhar Himmatrao Patil
Pushkar Bansidhar Patil
Anu G Pillai
Roshni Rahangdale
Mainak Sadhya
Sourabh Sahu
Amin Sakhaei
Rishab Sarkar
Ruhi Uzma Sheikh
Richa Ravi Siddannavar
G Sreelekha
P V Sudeep
Saurabh Tewari
Shruti Tiwari
Prajakta Upadhye
Amol D Vibhute
CH001.pdf
Chapter Basics of image analysis and manipulation using Python
1.1 Introduction
1.2 Image analysis and manipulation
1.2.1 Applications
1.3 Python—features, application
1.3.1 Python
1.3.2 Python applications
1.3.3 Python libraries
1.4 OpenCV package
1.4.1 OpenCV installation
1.4.2 Importing the module from package
1.4.3 Reading and displaying image with different libraries
1.4.4 Image properties
1.4.5 Image resolution:
1.4.6 Opening in grayscale mode
1.4.7 Save image
1.5 Some Python code and explanation
1.5.1 Image sharpening
1.5.2 Image resizing
1.5.3 Image blurring
1.5.4 Image watermarking
1.6 Conclusion
References
CH002.pdf
Chapter Digital image processing using Python language
2.1 Introduction
2.2 Python and its libraries for image processing
2.2.1 OpenCV
2.2.2 NumPy
2.3 Various operations for image processing using different libraries
2.3.1 Read, display and save operations using OpenCV
2.3.2 Applications of image processing in OpenCV
2.4 Conclusion
References
CH003.pdf
Chapter Review and implementation of image segmentation techniques in Python
3.1 Introduction
3.2 Thresholding based image segmentation
3.3 Python implementation
3.4 Edge detection
3.5 Machine learning based image segmentation
3.6 Unsupervised and semi-supervised machine learning approaches
3.7 Clustering algorithm using KMC
3.8 Implementation using Python
3.9 Summary
References
CH004.pdf
Chapter Segmentation of digital images with region growing algorithm
4.1 Introduction
4.2 Pre-processing methods for image segmentation
4.2.1 Converting to grayscale
4.2.2 Resize
4.2.3 Histogram equalization
4.2.4 Edge detection
4.2.5 Smoothing filter
4.2.6 Zero padding
4.3 Image segmentation methods
4.4 The proposed method
4.4.1 Pre-processing Python code (resize and histogram equalization)
4.4.2 Pre-processing Python code (edge detection and blurring)
4.4.3 Region growing Python code
4.4.4 Combine Python code
4.5 Conclusion
Acknowledgments
References
CH005.pdf
Chapter Retinal layer segmentation in OCT images
5.1 Introduction
5.2 Related works
5.3 Retinal layer segmentation using U-Net model
5.4 Design cycle
5.4.1 Problem definition
5.4.2 Data collection and preprocessing
5.4.3 Model definition
5.4.4 Model training
5.4.5 Experimental results
5.5 Conclusion
Acknowledgments
References
CH006.pdf
Chapter Image denoising using wavelet thresholding technique in Python
6.1 Introduction
6.2 Literature review
6.3 Methodology
6.3.1 Wavelet transform
6.3.2 Discrete wavelet transform
6.3.3 Wavelet decomposition
6.3.4 Wavelet reconstruction
6.3.5 Wavelet based de-noising technique
6.3.6 Hard thresholding
6.3.7 Soft thresholding
6.3.8 Wavelet families
6.4 Thresholding selection
6.4.1 Sure shrink
6.4.2 Bayes shrink
6.5 Python code for image de-noising
6.6 Results and discussion
References
CH007.pdf
Chapter Prostate cancer segmentation of peripheral zone and central gland regions in mpMRI: comparative analysis with deep neural network U-Net and its advanced models
7.1 Introduction
7.2 Literature reviews
7.3 Materials
7.3.1 Dataset details
7.3.2 Reference repository details
7.4 Methodology
7.4.1 Segmentation models
7.4.2 Classification models
7.4.3 Algorithm
Algorithm 1. Cancer cell detection and classification
7.4.4 Evaluation metrics
7.5 Results
7.5.1 Segmentation results
7.5.2 Classification results
7.6 Conclusion
7.6.1 Scope for future work
References
CH008.pdf
Chapter Optical character recognition: transforming images into text
8.1 Introduction
8.2 Implementation
8.2.1 OCR Model Structure (flow diagram)
8.2.2 Explanation
8.3 Model deployment
8.3.1 Steps for prediction
8.3.2 Python code for prediction of image
8.4 Model testing
8.4.1 TEST1
8.4.2 TEST2
8.4.3 TEST3
8.4.4 TEST4
8.5 Conclusion
References
CH009.pdf
Chapter Automatic COVID-19 identification with a binary neural network using CT images
9.1 Introduction
9.2 Related work
9.3 Methodology
9.3.1 Datasets
9.3.2 Binary neural networks (BNNs)
9.4 Binary layers implementation
9.4.1 QuantConv2D (QC)
9.4.2 Batch normalization (BN)
9.4.3 Max pooling layers (MPLs)
9.4.4 Fully connected layers (FCLs)
9.4.5 Quant dense layer (QD)
9.4.6 Activation function
9.5 Result
9.5.1 Python pseudo code
9.6 Conclusion
References
CH010.pdf
Chapter A review and implementation of image despeckling methods
10.1 Introduction
10.2 Speckle noise
10.2.1 Speckle noise characteristics in medical images
10.2.2 Speckle noise model
10.3 Speckle noise reduction methods
10.3.1 Conventional despeckling methods
10.3.2 Data-driven despeckling approaches
10.4 Experimental results
10.5 Conclusions
Acknowledgments
References
CH011.pdf
Chapter Application of image processing and machine learning techniques for vegetation cover classification in precision agriculture
11.1 Introduction
11.2 Revolutionizing crop classification in agriculture through artificial intelligence
11.3 Data collection and preprocessing
11.3.1 Study area
11.3.2 Dataset collection
11.3.3 Localization and selection of area of interest (AOI) and extraction of NDVI time-series data
11.4 Feature extraction and selection
11.5 Methodology
11.5.1 Data loading and preprocessing:
11.5.2 Using deep learning model
11.6 Optimizing agriculture with advance machine learning techniques
11.7 Applications of machine learning in precision agriculture
11.7.1 Crop yield prediction
11.7.2 Soil moisture estimation
11.7.3 Crop disease detection
11.7.4 Weed detection and management
11.7.5 Precision livestock farming
11.8 Challenges and opportunities for machine learning in agriculture
11.8.1 Limitations and challenges
11.9 Conclusion
References