The BRADLEY DEPARTMENT of ELECTRICAL and COMPUTER ENGINEERING

ECE 6554 Advanced Computer Vision | ECE | Virginia Tech

Graduate PROGRAMS

Course Information

Description

Current and state-of-the-art trends in computer vision, particularly in object recognition and scene understanding. Application of approaches in computer vision to various automatic perception problems. Strengths and weaknesses of computer vision techniques. Open questions and future research directions.

Why take this course?

With the advent of cell phone cameras and large-scale digital visual media the need to analyze, understand and organize this content automatically is more important than ever before. Computer Vision is a growing and fast evolving field. This course serves the purpose of informing students of the state-of-art approaches in computer vision, and providing them a platform to read and critique current trends in the field. This will in turn lead to an understanding of the important open research problems, an important first step in starting to make research contributions to the field. Through in-class paper presentations and discussion and semester-long research projects, this course will improve students' critical thinking skills, presentation skills and problem solving skills, which are essential for a student to develop their own successful research.

Prerequisites

5554

5554 provides students with the necessary background in computer vision fundamentals.

Major Measurable Learning Objectives

  • describe state-of-the-art approaches in object recognition and scene understanding
  • discuss tools from other fields (e.g., machine learning) that are frequently used to solve computer vision problems
  • implement two approaches to address important problems in computer vision
  • discuss and critique research papers in computer vision
  • identify open research questions in computer vision

Course Topics

Topic

Percentage of Course

1. Object Recognition %
a. Single Object Recognition 20%
b. Single Object Detection 10%
2. Scene Understanding 20%
3. Machine Learning for Computer Vision 10%
4. Applications of Computer Vision 10%
5. Current Research in Computer Vision %
a. Critical Analysis of the state-of-the-art 15%
b. Identification of New Research Problems 15%