English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 1h 49m | 259 MB
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.
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
Course Overview
1 Course Overview
Working with Image Data
2 Module Overview
3 Prerequisites and Course Outline
4 Introducing scikit-image
5 Working with Images as NumPy Arrays
6 Masking Images Using Array Manipulation
7 Masking Color Images
8 Introducing Block Views and Pooling
9 Block Views and Pooling Operations
10 Contours
11 Convex Hull
12 Edge Detection
13 Roberts and Sobel Edge Detection
14 Canny Edge Detection
Object and Feature Detection
15 Module Overview
16 Feature Detection and Image Descriptors
17 Visualizing Daisy Descriptors on Images
18 Visualizing Hog Feature Descriptors
19 Corner Detection
20 Introducing Denoising Filters
21 Applying Denoising Filters
22 Morphological Reconstruction
23 Filling Holes and Finding Peaks Using Erosion and Dilation
Segmentation and Transformation
24 Module Overview
25 Introducing Thresholding
26 Applying Global and Local Thresholding Algorithms
27 Image Segmentation and Region Adjacency Graphs
28 Segmentation and Merging Segments Using Rags
29 Introducing Watershed Algorithms for Segmentation
30 Segmentation Using Classic and Compact Watershed
31 Applying Image Transformations
32 Introducing the MSE and SSIM as Distance Measures
33 Comparing Images Using MSE and SSIM
34 Summary and Further Study
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