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.
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