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Hands-On Image Processing with Python

You're reading from Hands-On Image Processing with Python Expert techniques for advanced image analysis and effective interpretation of image data

Product type Paperback

Published in Nov 2018

Publisher Packt

ISBN-13 9781789343731

Length 492 pages

Edition 1st Edition

Languages

Processing

Tools

Keras

Concepts

Computer Vision

Author (1):

Sandipan Dey

View More author details

Table of Contents (20) Chapters

Title Page

Copyright and Credits

Dedication

About Packt

Contributors

Preface

1. Getting Started with Image Processing FREE CHAPTER

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

Leave a review - let other readers know what you think

Index

Further reading

The following are the various references from various sources:

Lecture notes from http://fy.chalmers.se/~romeo/RRY025/notes/E1.pdf and http://web.pdx.edu/~jduh/courses/Archive/geog481w07/Students/Ludwig_ImageConvolution.pdf
These slides (https://web.cs.wpi.edu/~emmanuel/courses/cs545/S14/slides/lecture10.pdf) by Prof. Emmanuel Agu
This lecture from Oxford university: http://www.robots.ox.ac.uk/~az/lectures/ia/lect2.pdf

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Authors (1)

Sandipan Dey

Sandipan Dey

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.

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