ECE 4230 - Computer Vision

Fall. 4 credits. Student option grading.

Prerequisite: ECE 2720. Co-meets with ECE 5470.

A. Reeves.

A first course in computer vision for advanced undergraduate students; that is, the analysis by computer of multidimensional signals provided by imaging sensors including image sequences and three-dimensional images for primarily autonomous applications. Basic techniques for image processing and feature extraction are covered in lectures; topics include image formation, image filtering, edge detection, region growing, shape description, and machine learning for computer vision. Machine learning methods including convolution neural networks, deep learning models for focus attention and image segmentation. The course focus is on the machine interpretation of images for autonomous decision making.

Outcome 1: Demonstrate an understanding of precision image review and annotation.

Outcome 2: Demonstrate an understanding of image filtering and image segmentation.

Outcome 3: Demonstrate an understanding of deep learning methods for object identification and region segmentation.

Outcome 4: Demonstrate a knowledge the image analysis methods. for image sequences and 3D images.

Outcome 5: Demonstrate a knowledge in computer vision algorithm design and development including a quantitative evaluation of algorithm performance.