Hands-On Image Processing with Python

More Information
  • Perform basic data pre-processing tasks such as image denoising and spatial filtering in Python
  • Implement Fast Fourier Transform (FFT) and Frequency domain filters (e.g., Weiner) in Python
  • Do morphological image processing and segment images with different algorithms
  • Learn techniques to extract features from images and match images
  • Write Python code to implement supervised / unsupervised machine learning algorithms for image processing
  • Use deep learning models for image classification, segmentation, object detection and style transfer

Image processing plays an important role in our daily lives with various applications such as in social media (face detection), medical imaging (X-ray, CT-scan), security (fingerprint recognition) to robotics & space. This book will touch the core of image processing, from concepts to code using Python.

The book will start from the classical image processing techniques and explore the evolution of image processing algorithms up to the recent advances in image processing or computer vision with deep learning. We will learn how to use image processing libraries such as PIL, scikit-mage, and scipy ndimage in Python. This book will enable us to write code snippets in Python 3 and quickly implement complex image processing algorithms such as image enhancement, filtering, segmentation, object detection, and classification. We will be able to use machine learning models using the scikit-learn library and later explore deep CNN, such as VGG-19 with Keras, and we will also use an end-to-end deep learning model called YOLO for object detection. We will also cover a few advanced problems, such as image inpainting, gradient blending, variational denoising, seam carving, quilting, and morphing.

By the end of this book, we will have learned to implement various algorithms for efficient image processing.

  • Practical coverage of every image processing task with popular Python libraries
  • Includes topics such as pseudo-coloring, noise smoothing, computing image descriptors
  • Covers popular machine learning and deep learning techniques for complex image processing tasks
Page Count 492
Course Length 14 hours 45 minutes
ISBN 9781789343731
Date Of Publication 30 Nov 2018
Point-wise intensity transformations – pixel transformation
Histogram processing – histogram equalization and matching
Linear noise smoothing
Nonlinear noise smoothing
Further reading
Image derivatives – Gradient and Laplacian
Sharpening and unsharp masking
Edge detection using derivatives and filters (Sobel, Canny, and so on)
Image pyramids (Gaussian and Laplacian) – blending images
Further reading
What is image segmentation?
Hough transform – detecting lines and circles
Thresholding and Otsu's segmentation
Edges-based/region-based segmentation
Felzenszwalb, SLIC, QuickShift, and Compact Watershed algorithms 
Active contours, morphological snakes, and GrabCut algorithms
Further reading
Supervised versus unsupervised learning
Unsupervised machine learning – clustering, PCA, and eigenfaces
Supervised machine learning – image classification
Supervised machine learning – object detection
Further reading


Sandipan Dey

Sandipan Dey is a data scientist with a wide range of interests, covering topics such as machine learning, deep learning, image processing, and computer vision. He has worked in numerous data science fields, working with recommender systems, predictive models for the events industry, sensor localization models, sentiment analysis, and device prognostics. He earned his master's degree in computer science from the University of Maryland, Baltimore County, and has published in a few IEEE Data Mining conferences and journals. He has earned certifications from 100+ MOOCs on data science, machine learning, deep learning, image processing, and related courses/specializations. He is a regular blogger on his blog (sandipanweb) and is a machine learning education enthusiast.