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.
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.
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%|