Building Image Processing Applications Using scikit-image

In this course, you'll explore the scikit-image Python library which allows you to apply sophisticated image processing techniques to images and to quickly extract important insights or pre-process images for input to machine learning models.
Course info
Rating
(14)
Level
Beginner
Updated
Nov 20, 2018
Duration
1h 49m
Table of contents
Description
Course info
Rating
(14)
Level
Beginner
Updated
Nov 20, 2018
Duration
1h 49m
Description

In this course, Building Image Processing Applications using scikit-image, you’ll gain an understanding of a few core image processing techniques and see how these techniques can be implemented using the scikit-image Python library. First, you’ll learn the basics of working with image data represented in the form of multidimensional arrays. Next, you’ll discover to manipulate images using the NumPy package, extract features using block view and pooling techniques, detect edges and lines and find contours in images. Then, you’ll explore various object and feature detection techniques using the DAISY and HOG algorithms to extract image features, along with using morphological reconstruction to fill holes and find peaks in your images. Finally, you'll delve into image processing techniques that allow you to segment similar regions in your images and apply complex transformations by exploring the Regional Adjacency Graph data structure to represent image segments. By the end of this course, you’ll have a better understanding of a range of image processing techniques that you can use on your images, and you’ll be able to implement all of those using scikit-image.

About the author
About the author

A problem solver at heart, Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework.

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Section Introduction Transcripts
Section Introduction Transcripts

Course Overview
Hi. My name is Janani Ravi, and welcome to this course on building image processing applications using scikit-image. A little about myself, I have a master's degree in electrical engineering from Stanford and have worked at companies such as Microsoft, Google, and Flipkart. At Google, I was one of the first engineers working on real time collaborative editing in Google Docs, and I hold four patents for its underlying technologies. I currently work on my own startup, Loonycorn, a studio for high-quality video content. In this course, you will learn a variety of image processing techniques that will allow you to quickly and efficiently extract information from a huge image data set. We'll start off with the basics of working with image data, represented in the form of multidimensional arrays. We'll manipulate images using the NumPy package, extract features using block view and pooling techniques, detect edges and lines, and find contours and images. We'll then move on to object and feature detection techniques. We'll use the DAISY and HOG algorithms to extract image features. We'll detect corners using Sobel's, as well as Robert's techniques, and use morphological reconstruction to fill holes and find peaks in our images. We'll then study image processing techniques that allow us to segment similar regions in our images and apply complex transformations. In this context, we'll study the region adjacency graph data structure to represent image segments. We'll also compare how similar images are using two techniques, the SSIM and the MSC. At the end of this course, you'll have a good understanding of a range of image processing techniques that you can use on your images, and you'll be able to implement all of these using scikit-image.