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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

Blob detectors with LoG, DoG, and DoH


In an image, a blob is defined as either a bright on a dark region, or a dark on a bright region. In this section, we will discuss how to implement blob features detection in an image using the following three algorithms. The input image is a colored (RGB) butterfly image.

Laplacian of Gaussian (LoG)

In the Chapter 3Convolution and Frequency Domain Filtering, we saw that the cross correlation of an image with a filter can be viewed as pattern matching; that is, comparing a (small) template image (of what we want to find) against all local regions in the image. The key idea in blob detection comes from this fact. We have already seen how an LoG filter with zero crossing can be used for edge detection in the last chapter. LoG can also be used to find scale invariant regions by searching 3D (location + scale) extrema of the LoG with the concept of Scale Space. If the scale of the Laplacian (σ of the LoG filter) gets matched with the scale of the blob, the...

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