Book Image

Hands-On Image Processing with Python

By : Sandipan Dey
Book Image

Hands-On Image Processing with Python

By: Sandipan Dey

Overview of this book

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.
Table of Contents (20 chapters)
Title Page
Copyright and Credits
Dedication
About Packt
Contributors
Preface
Index

Questions


  1. Use k-means clustering for thresholding an image (use number of clusters=2) and compare the result with Otsu's.
  2. Use scikit-learn's cluster.MeanShift() and mixture.GaussianMixture() functions to segment an image with mean shift and GMM-EM clustering methods, respectively—another two popular clustering algorithms.
  3. Use Isomap (from sklearn.manifold) for non-linear dimension reduction and visualize 2-D projections. Is it better than linear dimension reduction with PCA? Repeat the exercise with TSNE (again from sklearn.manifold).
  4. Write a Python program to show that the weighted linear combination of a few dominating eigenfaces indeed approximates a face.
  5. Show that eigenfaces can also be used for naive face-detection (and recognition) and write Python code to implement this (hint—refer to this article: https://sandipanweb.wordpress.com/2018/01/06/eigenfaces-and-a-simple-face-detector-with-pca-svd-in-python/).
  6. Use PCA to compute eigendigit-based vectors from the MNIST dataset (this is similar...