Course: EW453 Intro to Computer Vision     

3 Credits – 2 Recitation Hours – 2 Laboratory Hours

Course Description: 

This foundational course covers the technical aspects of image processing, pattern recognition, and machine learning in computer vision. Students will learn essential algorithms and techniques for applications in automation, remote sensing, and medical imaging. [fall, spring]

Pre-requisites: 

EW200 or any computer programming class

Course Coordinator: 

Prof. Mwaffo

Textbook: 

None

Course Objectives: 

1. Understand the fundamentals of computer vision tools and techniques.

2. Become proficient in a variety of image processing methods.

3. Develop skills for image understanding, object recognition and machine learning.

4. Develop a working knowledge of current technical issues in the field of computer vision.

Topics: 

1. Color Spaces and Color Based Object Detection.

2. Machine learning: Classifiers and Feature Selection.

3. Feature types: Edge Detection, General Filtering, Morphology, Texture, Object characteristics.

4. Classical Object detection methods: Histogram of Oriented Gradients, Template matching, Harr Cascade Classifier.

5. Deep Neural Networks theory and application: Convolutional Neural Networks (CNNs), Region-based Convolutional Neural Networks (R-CNNs), Single Shot MultiBox Detector (SSD), You Only Look Once (YOLO).

6. Motion detection, Background subtraction, Green screen.

7. Stereo depth perception.