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

Summary


In this chapter, the recent advances in image processing with deep learning models were introduced. We started by discussing the basic concepts of deep learning, how it's different from traditional ML, and why we need it. Then CNNs were introduced as deep neural networks designed particularly to solve complex image processing and computer vision tasks. The CNN architecture with convolutional, pooling, and FC layers were discussed. Next, we introduced TensorFlow and Keras, two popular deep learning libraries in Python. We showed how test accuracy on the MNIST dataset for handwritten digits classification can be increased with CNNs, then the same using FC layers only. Finally, we discussed a few popular networks such as VGG-16/19, GoogleNet, and ResNet. Kera's VGG-16 model was trained on Kaggle's Dogs vs. Cats competition images and we showed how it performs on the validation image dataset with decent accuracy.

In the next chapter, we'll discuss how to solve more complex image processing...