A Notebook Series on Computer Vision
Highlights
Acknowledgement
I dedicate this work to my teacher Prof. B. Krishna Mohan, who is the source of whatever little I know of this amazing field called Computer Vision and under whom I have completed my master's degree.
About the Content
This notebook series is a collection of Jupyter notebooks organized in chapters, covering some of the most interesting and useful topics of Computer Vision.
This series focuses on presenting concepts as organically connected tools to solve different problems related to RGB images. And give to develop an understanding in the reader about how different concepts are tied together to implement one image analytics solution.
The notebooks presented here are a blend of theory and code to balance between understanding and implementation.
Objectives
The objective of this notebook series is to make the reader comfortable with different concepts of image processing using traditional and neural network based techniques.
Software Pre-requisites
You will need a Python environment with OpenCV, Jupyter notebooks, Keras, Tensorflow installed.
Use the requirements.txt in the repo.
Knowledge Pre-requisites
Working knowledge of Python and Jupyter notebooks is essential for this course. Hands-on experience with OpenCV and Keras will also be helpful.
Tutorial Outline
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Chapter 1 - Foundation
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Chapter 2 - Neighborhood Operations
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Chapter 3 - Image Segmentation
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Chapter 4 - Video Analytics
About the Author
Ankur Shukla is a Data Science Analyst at Deloitte Consulting. He consult clients from different industries on their data science problems. Python is his bread and butter and he uses it extensively for his day to day machine learning and data analysis tasks. Ankur is a postgraduate from CSRE, IIT Bombay in Geoinformatics and Natural Resources Engineering. Majority of his work at CSRE was focused in satellite image processing using Python.