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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
Index

Thresholding and Otsu's segmentation


Thresholding refers to a family of algorithms that use a pixel value as a threshold to create a binary image (an image with only black-and-white pixels) from a grayscale image. It provides the simplest way to segment objects from a background in an image. The threshold can be chosen manually (by looking at the histogram of pixel values) or automatically using algorithm. In scikit-image, there are two categories of thresholding algorithm implementations, namely histogram-based (a pixel intensity histogram is used with some assumptions of the properties of this histogram, for example bimodal) and local (only the neighboring pixels are used to process a pixel; it makes these algorithms more computationally expensive).

In this section, we shall only discuss a popular histogram-based thresholding method known as Otsu's method (with the assumption of a bimodal histogram). It computes an optimal threshold value by simultaneously maximizing the inter-class variance...

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