ECE: Electrical & Computer Engineering

ECE 5554 Computer Vision

Spring 2016 textbook list

The Spring 2016 ECE textbook list is available online for students.

Current Prerequisites & Course Offering

For current prerequisites for a particular course, and to view course offerings for a particular semester, see the Virginia Tech Course Timetables.

Return to course list

ECE 5554 Computer Vision (3C)

Techniques for automated analysis of images and videos. Image formation, feature detection, segmentation, multiple view geometry, recognition, and video processing.

What is the reason for this course?

Computers increasingly require the ability to "see" their surroundings in order to interact with humans and with the three-dimensional world. This course introduces theory and techniques for analyzing the content of images. Applications of computer vision include robotics, autonomous vehicle navigation, industrial automation, content-based search in image databases, face and gesture recognition, and aides for the seeing-impaired.

Typically offered: Fall. Program Area: Computers.

Prerequisites: Graduate standing.

Why are these prerequisites or corequisites required?

This material is appropriate for the 5000 level. This class will apply extensive and in-depth knowledge that builds on undergraduate learning through a conceptual understanding of the specialization.

Department Syllabus Information:

Major Measurable Learning Objectives:
  • contrast common image formation models
  • implement various ways of extracting features from images
  • segment an image into meaningful regions
  • derive the theory behind multi-view geometry
  • implement various approaches to recognizing objects and scenes in images
  • implement techniques for processing video sequences

Course Topics
Topic Percentage
1. Features and filters: linear filters, edge detection, binary image analysis, image pyramids, texture 20%
2. Grouping and fitting: fitting lines and curves, robust fitting, RANSAC, Hough transform, segmentation, clustering 20%
3. Multiple views and motion: dense motion estimation, optical flow, camera model, image formation, planar homography, image warping, Epipolar geometry, stereo and multi-view reconstruction, invariant local features 20%
4. Recognition: instance recognition with local features, bag-of-words representations, shape matching, part-based models, face detection and recognition, sliding window detection 20%
5. Video processing: motion descriptors, tracking, background subtraction, activity recognition 20%

Return to course list