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Hands-On Image Processing with Python

You're reading from  Hands-On Image Processing with Python

Product type Book
Published in Nov 2018
Publisher Packt
ISBN-13 9781789343731
Pages 492 pages
Edition 1st Edition
Languages
Concepts
Author (1):
Sandipan Dey Sandipan Dey
Profile icon Sandipan Dey
LinkedIn
Sandipan Dey is a data scientist with a wide range of interests, covering topics such as machine learning, deep learning, image processing, and computer vision. He has worked in numerous data science fields, working with recommender systems, predictive models for the events industry, sensor localization models, sentiment analysis, and device prognostics. He earned his master's degree in computer science from the University of Maryland, Baltimore County, and has published in a few IEEE Data Mining conferences and journals. He has earned certifications from 100+ MOOCs on data science, machine learning, deep learning, image processing, and related courses. He is a regular blogger (sandipanweb) and is a machine learning education enthusiast.
See other products by Sandipan Dey

Table of Contents (20) Chapters

Title Page
Copyright and Credits
Dedication
About Packt
Contributors
Preface
1. Getting Started with Image Processing 2. Sampling, Fourier Transform, and Convolution 3. Convolution and Frequency Domain Filtering 4. Image Enhancement 5. Image Enhancement Using Derivatives 6. Morphological Image Processing 7. Extracting Image Features and Descriptors 8. Image Segmentation 9. Classical Machine Learning Methods in Image Processing 10. Deep Learning in Image Processing - Image Classification 11. Deep Learning in Image Processing - Object Detection, and more 12. Additional Problems in Image Processing 1. Other Books You May Enjoy Index

Chapter 5. Image Enhancement Using Derivatives

In this chapter, we shall continue our discussion on image enhancement, which is the problem of improving the appearance or usefulness of an image. We shall concentrate mainly on spatial filtering techniques to compute image gradients/derivatives, and how these techniques can be used for edge detection in an image. First, we shall start with the basic concepts of image gradients using the first order (partial) derivatives, how to compute the discrete derivatives, and then discuss the second order Derivative/Laplacian. We shall see how they can be used to find edges in an image. Next, we shall discuss a few ways to sharpen/unsharp mask an image using the Python image processing librariesPIL, the filter module of scikit-image, and thendimage module of SciPy. Next, we shall see how to use different filters (sobel, canny, LoG, and so on) and convolve them with the image to detect edges in an image. Finally, we shall discuss how to compute Gaussian...

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Authors (1)

Profile icon Sandipan Dey
LinkedIn
Sandipan Dey is a data scientist with a wide range of interests, covering topics such as machine learning, deep learning, image processing, and computer vision. He has worked in numerous data science fields, working with recommender systems, predictive models for the events industry, sensor localization models, sentiment analysis, and device prognostics. He earned his master's degree in computer science from the University of Maryland, Baltimore County, and has published in a few IEEE Data Mining conferences and journals. He has earned certifications from 100+ MOOCs on data science, machine learning, deep learning, image processing, and related courses. He is a regular blogger (sandipanweb) and is a machine learning education enthusiast.
Read more
See other products by Sandipan Dey

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