Building Image Processing Applications Using scikit-image

سرفصل های دوره

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.

1. Course Overview

1. Course Overview

2. Working with Image Data

01. Version Check

02. Module Overview

03. Prerequisites and Course Outline

04. Introducing scikit-image

05. Working with Images as NumPy Arrays

06. Masking Images Using Array Manipulation

07. Masking Color Images

08. Introducing Block Views and Pooling

09. Block Views and Pooling Operations

10. Contours

11. Convex Hull

12. Edge Detection

13. Roberts and Sobel Edge Detection

14. Canny Edge Detection

3. Object and Feature Detection

1. Module Overview

2. Feature Detection and Image Descriptors

3. Visualizing Daisy Descriptors on Images

4. Visualizing Hog Feature Descriptors

5. Corner Detection

6. Introducing Denoising Filters

7. Applying Denoising Filters

8. Morphological Reconstruction

9. Filling Holes and Finding Peaks Using Erosion and Dilation

4. Segmentation and Transformation

01. Module Overview

02. Introducing Thresholding

03. Applying Global and Local Thresholding Algorithms

04. Image Segmentation and Region Adjacency Graphs

05. Segmentation and Merging Segments Using Rags

06. Introducing Watershed Algorithms for Segmentation

07. Segmentation Using Classic and Compact Watershed

08. Applying Image Transformations

09. Introducing the MSE and SSIM as Distance Measures

10. Comparing Images Using MSE and SSIM

11. Summary and Further Study

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تولید کننده: PluralSight