Computer Vision

This course provides an overview of computer vision on modern Intel® architecture. Topics include: understanding how to use computer vision in industry; learning the main algorithms for image processing; exploring how machine learning is used in computer vision.

1. History of computer vision.

* How the modern industry uses computer vision

* Significant technologies and libraries

* Computer vision application development workflow

2. Core techniques of image processing.

* Methods such as interpolation, color conversions, and thresholding

* How to implement these tools in NumPy and Matplotlib

3. Image transformations and their usages.

* How kernel methods (such as convolution) work and their impact on

computer vision

* How to use image gradients to perform edge detection

* Learn more about edge detection by using Canny edge detectors

* Understand more advanced techniques through Fourier transformations

and wavelet methods

4. Image contours, segmentation, and image matching.

* An overview of image contours and the techniques to find them

* How segment the foreground and background to select areas for analysis

5. Image features and the techniques to find them. Learn about:

* The methods to perform object detection and object recognition

6. Machine learning algorithms for computer vision. Topics include:

* Support vector machines (SVM)—a popular algorithm used for

classification problems

* K-nearest neighbor clustering for image analysis

* Unsupervised and supervised learning techniques

7. Application of neural networks. Learn about:

* The mathematical theory supporting neural networks

* How to use convolutional neural networks for image classification

By the end of this course, students will have practical knowledge of: different techniques to process, transform, and classify images, how to apply deep learning to visual tasks, mportant computer vision methods, such as image segmentation and edge extraction.