In this chapter, we discussed different image processing techniques based on mathematical morphology. We discussed morphological binary operations such as erosion, dilation, opening, closing, skeletonizing, and white and black top-hats. Then we discussed some applications such as computing the convex hull, removing small objects, extracting the boundary, fingerprint cleaning with opening and closing, filling holes in binary objects, and using opening and closing to remove noise. After that, we discussed extension of the morphological operations to grayscale operations and applications of morphological contrast enhancement, noise removal with the median filter, and computing local entropy. Also, we discussed how to compute the morphological (Beucher) gradient and the morphological Laplace. By the end of this chapter, the reader should be able to write Python code for...
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Book Overview & Buying
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Table Of Contents
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 (14 chapters)
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
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