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

Chapter 10. Deep Learning in Image Processing - Image Classification

In this chapter, we shall discuss recent advances in image processing with deep learning. We'll start by differentiating between classical and deep learning techniques, followed by a conceptual section on convolutional neural networks (CNN), the deep neural net architectures particularly useful for image processing. Then we'll continue our discussion on the image classification problem with a couple of image datasets and how to implement it with TensorFlow and Keras, two very popular deep learning libraries. Also, we'll see how to train deep CNN architectures and use them for predictions.

 The topics to be covered in this chapter are as follows:

  • Deep learning in image processing
  • CNNs
  • Image classification with TensorFlow or Keras with the handwritten digits images dataset
  • Some popular deep CNNs (VGG-16/19, InceptionNet, ResNet) with an application in classifying the cats versus dogs images with the VGG-16 network