(Computer) Vision for Intelligent Robotics, Fall 2017
Course number: Info I590 / CS B659
Meets: Tuesday/Thursday 4:00-5:15pm
Location: BH 233
Instructor: Prof. Michael S. Ryoo
Email: mryoo "at" indiana.edu
Office: Informatics E259
Office hours: Friday 2-3pm
In this graduate seminar course, we will review and discuss state-of-the-art computer vision methodologies as well as their applications to robots. Specific topics will include object recognition, activity recognition, deep learning for both images and videos, and first-person vision for wearable devices and robots. The objective of the course is to understand important problems in computer vision and intelligent robotics, discuss advantages and disadvantages of existing approaches, and identify open questions and future research directions.
Interest in computer vision; basic programming skills; ability to read and understand conference papers. This course will focus on deep learning techniques and their robotics applications, which will extend topics covered in other computer vision courses including B490/B659. Any previous experience in computer vision, machine learning, and robot vision will be a plus.
Please talk to me if you are unsure if the course is a good match for your background.
1. Understanding images and videos
Image features and object classification
Action recognition from videos
More deep learning models
2. Robot learning
Robot perception: first-person recognition
Deep reinforcement learning
Deep learning for robot action
No class - Thanksgiving
Deep learning for robot action (cont’d)
Final project presentations
Course requirements and grading:
Paper/experiment presentations (30%): each student is expected to provide ~2 presentations throughout the course. A student may choose to provide either (1) paper presentation or (2) experiment presentation (i.e., presenting the results obtained by testing the method's code on existing datasets) for their presentations.
Paper review and class participation (20%): the students are required to choose a paper per class and submit its short review before the class.
Final project (50%): each student will choose his/her individual research topic and do research. This can be as simple as implementing several previous methods and comparing them, and can be as serious as proposing new concepts and algorithms, implementing them, and evaluating them with public datasets to advance the state-of-the-arts.