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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

Supervised machine learning – object detection


So far, we have demonstrated how to use the classification model to classify an image, for example, to use binary classification to find if an image contains the handwritten digit 1 or not. In the next section, we will see how to use supervised machine learning models, not only to check whether an object is in an image or not, but also to find the location of the object in the image (for example, in terms of a bounding box—a rectangle the object is contained in).

Face detection with Haar-like features and cascade classifiers with AdaBoost – Viola-Jones

As we have discussed briefly in Chapter 7Extracting Image Features and Descriptors, (in the context of Haar-like feature extraction), the Viola-Jones' object detection technique can be used for face detection in images. It is a classical machine learning approach, where a cascade function is trained using a training set of positive and negative images, by using the hand-crafted Haar-like features...

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