In this chapter, we discussed a few important feature detection and extraction techniques to compute different types of feature descriptors from an image using Python's scikit-image
and cv2 (python-opencv)
libraries. We started with the basic concepts of local feature detectors and descriptors for an image, along with their desirable properties. Then we discussed the Harris Corner Detectors to detect corner interest points of an image and use them to match two images (with the same object captured from different viewpoints). Next, we discussed blob detection using LoG/DoG/DoH filters. Next, we discussed HOG, SIFT, ORB, BRIEF binary detectors/descriptors and how to match images with these features. Finally, we discussed Haar-like features and face detection with the Viola—Jones algorithm. By the end of this chapter, you should be able to compute different features/descriptors of an image with Python libraries. Also, you should be able to match images with different types of feature...
Hands-On Image Processing with Python
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Hands-On Image Processing with Python
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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
Free Chapter
Getting Started with Image Processing
Sampling, Fourier Transform, and Convolution
Convolution and Frequency Domain Filtering
Image Enhancement
Image Enhancement Using Derivatives
Morphological Image Processing
Extracting Image Features and Descriptors
Image Segmentation
Classical Machine Learning Methods in Image Processing
Deep Learning in Image Processing - Image Classification
Deep Learning in Image Processing - Object Detection, and more
Additional Problems in Image Processing
Other Books You May Enjoy
Index
Customer Reviews