Building Image Processing Applications Using scikit-image

[mitsumi]

Janani Ravi | Duration: 1:49 h | Video: H264 1280x720 | Audio: AAC 44,1 kHz 2ch | 259 MB | Language: English

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.

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