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Hands-On Image Processing with Python

Hands-On Image Processing with Python

By : Sandipan Dey
3 (5)
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Hands-On Image Processing with Python

Hands-On Image Processing with Python

3 (5)
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)
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Title Page
Copyright and Credits
Dedication
About Packt
Contributors
Preface
2
Index

Chapter 11. Deep Learning in Image Processing - Object Detection, and more

In this chapter, we'll continue our discussion on the recent advances in image processing with deep learning. We will be dealing with a few problems in particular, and shall try to solve them using deep learning with deep CNNs. 

We will look at the object detection problem, understanding the basic concepts involved, then examine how to write code to solve the problem with object proposals and a You Only Look On (YOLO) v2 pre-trained deep neural network in Keras. You will be provided with resources that will help you in training the YOLO net.

Get ready to learn about transfer learning and solve deep segmentation problems using the DeepLab library. You will learn to specify which layers to train while training a deep learning model, and demonstrate a custom image classification problem by only learning the weights for the FC layers of a VGG16 network.

You may be surprised to learn how deep learning can be used in art generation...

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