Computer vision and machine-learning techniques have revolutionized our world in recent years. However, for robust operation, these techniques need very large amounts of data and annotation. This requirement places real limits on their utility in critical situations where access to data may be limited.
Training the next generation of STEM researchers in visual media experiences from new forms of imaging to augmented and mixed reality requires integrating knowledge from several disciplines and effectively communicating that information to students and trainees.
In the last 50 years, the rise of computing and artificial intelligence (AI) has transformed our society. Futuristic applications such as autonomous vehicles are now driving on Arizona roadways, websites can now recognize and identify faces in an image, and our stock trading is driven by machine learning algorithms.