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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

Feature detectors versus descriptors


In image processing, (local) features refer to a group of key/salient points or information relevant to an image processing task, and they create an abstract, more general (and often robust) representation of an image. A family of algorithms that choose a set of interest points from an image based on some criterion (for example, cornerness, local maximum/minimum, and so on, that detect/extract the features from an image) are called feature detectors/extractors.

 

 

On the contrary, a descriptor consists of a collection of values to represent the image with the features/interest points (for example, HOG features). Feature extraction can also be thought of as an operation that transforms an image into a set of feature descriptors, and, hence, a special form of dimensionality reduction. A local feature is usually formed by an interest point and its descriptor together.

Global features from the whole image (for example, image histogram) are often not desirable...

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