Computer vision is the field of computer science that focuses on replicating parts of the complexity of the human vision system and enabling computers to identify and process objects in images and videos in the same way that humans do. Until recently, computer vision only worked in limited capacity.

Thanks to advances in artificial intelligence and innovations in deep learning and neural networks, the field has been able to take great leaps in recent years and has been able to surpass humans in some tasks related to detecting and labeling objects.

One of the driving factors behind the growth of computer vision is the amount of data we generate today that is then used to train and make computer vision better.

Along with a tremendous amount of visual data (more than 3 billion images are shared online every day), the computing power required to analyze the data is now accessible. As the field of computer vision has grown with new hardware and algorithms so has the accuracy rates for object identification. In less than a decade, today’s systems have reached 99 percent accuracy from 50 percent making them more accurate than humans at quickly reacting to visual inputs.

Early experiments in computer vision started in the 1950s and it was first put to use commercially to distinguish between typed and handwritten text by the 1970s, today the applications for computer vision have grown exponentially.

Our Publications

• Scorsoglio, A., Furfaro, R., Linares, R., & Gaudet, B. (2020). Image-based Deep Reinforcement Learning for Autonomous Lunar Landing. In AIAA Scitech 2020 Forum (p. 1910). DOI: http://dx.doi.org/10.2514/6.2020-1910
• Drozd K., Furfaro R., & Lambert D. (2019). Modeling Imaging Uncertainty for OSIRIS-REx's Asteroid Approach Observations. Conference: 29th AAS/AIAA Space Flight Mechanics Meeting. Ka'anapali, HI. PDF
• Campbell, T., Furfaro, R., Linares, R., & Gaylor, D. (2017, January). A deep learning approach for optical autonomous planetary relative terrain navigation. In 27th AAS/AIAA Space Flight Mechanics Meeting, 2017 (pp. 3293-3302). Univelt Inc. PDF