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.
What you'll learn
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.
Table of contents
- Version Check 0m
- Module Overview 1m
- Prerequisites and Course Outline 2m
- Introducing scikit-image 5m
- Working with Images as NumPy Arrays 6m
- Masking Images Using Array Manipulation 4m
- Masking Color Images 4m
- Introducing Block Views and Pooling 3m
- Block Views and Pooling Operations 4m
- Contours 6m
- Convex Hull 3m
- Edge Detection 4m
- Roberts and Sobel Edge Detection 1m
- Canny Edge Detection 6m
- Module Overview 1m
- Feature Detection and Image Descriptors 3m
- Visualizing Daisy Descriptors on Images 3m
- Visualizing Hog Feature Descriptors 3m
- Corner Detection 5m
- Introducing Denoising Filters 2m
- Applying Denoising Filters 5m
- Morphological Reconstruction 2m
- Filling Holes and Finding Peaks Using Erosion and Dilation 5m
- Module Overview 1m
- Introducing Thresholding 2m
- Applying Global and Local Thresholding Algorithms 4m
- Image Segmentation and Region Adjacency Graphs 2m
- Segmentation and Merging Segments Using Rags 5m
- Introducing Watershed Algorithms for Segmentation 2m
- Segmentation Using Classic and Compact Watershed 3m
- Applying Image Transformations 3m
- Introducing the MSE and SSIM as Distance Measures 3m
- Comparing Images Using MSE and SSIM 6m
- Summary and Further Study 2m
About the author
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. After spending years working in tech in the Bay Area, New York, and Singapore at companies such as Microsoft, Google, and Flipkart, Janani finally decided to combine her love for technology with her passion for teaching. She is now the co-founder of Loonycorn, a content studio focused on providing ... more