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

Deep semantic segmentation with DeepLab V3+


In this section, we'll discuss how to use a deep learning FCN to perform semantic segmentation of an image. Before diving into further details, let's clear the basic concepts.

Semantic segmentation

Semantic segmentation refers to an understanding of an image at pixel level; that is, when we want to assign each pixel in the image an object class (a semantic label). It is a natural step in the progression from coarse to fine inference. It achieves fine-grained inference by making dense predictions that infer labels for every pixel so that each pixel is labeled with the class of its enclosing object or region.

 

 

DeepLab V3+

DeepLab presents an architecture for controlling signal decimation and learning multi-scale contextual features. DeepLab uses an ResNet-50 model, pre-trained on the ImageNet dataset, as its main feature extractor network. However, it proposes a new residual block for multi-scale feature learning, as shown in the following diagram....