U of G Shaping Future of Computer Vision in Canada

In healthcare, AI-powered imaging systems assist with detecting abnormalities in radiology scans, segmenting organs, and tracking disease progression. In manufacturing, automated visual inspection systems can identify microscopic defects and ensure quality control at speeds that are beyond human capabilities.

In agriculture, vision-guided robots detect weeds, diseased plants, pests and assess crop health, which is very beneficial to Canada’s robust agri-food sector. Meanwhile, geospatial and environmental applications of computer vision are using satellite and drone imagery to monitor wildfires, land use, infrastructure, and biodiversity. 

These activities fall within the categories of computer vision, and more broadly spatial intelligence, which are fields of study within artificial intelligence (AI) that focuses on enabling computers to be able to “see,” similarly to how humans can. Computer vision enables machines to interpret and analyze images and videos (2D), ideally recognizing objects, faces, colours, movement, facial expressions and context. Spatial intelligence however builds on computer vision by transforming visual perception from images into 3D spatial understanding of environments and objects. Computer vision is core to the technology in sectors like healthcare, manufacturing, agriculture, and environmental monitoring, where  visual data is central to operations.

A workforce analysis supported by the Vector Institute and the Conference Board of Canada reports a 37 per cent increase in demand for core AI skills between 2018 and 2023. According to Statistics Canada, image or pattern recognition, which falls under computer vision, was used by 11 to 22 per cent of businesses that reported AI adoption in 2024 and 2025. In professional, scientific and technical services using AI, 47 per cent reported using image and pattern recognition in 2024. 

Dr. Neil Bruce in U of G’s School of Computer Science studies visual attention and saliency modeling, which are foundational techniques that help systems determine which regions of an image are most informative. This is particularly helpful in healthcare, where identifying the most informative regions of medical images can help prioritize areas of concern in radiology scans.

Dr. Minglun Gong’s research interests cover computer vision, computer graphics, and artificial intelligence. In recent years, he has developed novel neural network-based regression models for crowd and object counting and tracking, as well as generative models for 3D scene capture and modelling. This work is particularly relevant to spatial intelligence and precision agriculture. 

Dr. Yulia Kotseruba focuses on cognitively inspired computer vision. Her research examines the behaviours of drivers and pedestrians in traffic to develop AI models for assistive and automated driving. 

Dr. Gurjit Randhawa develops intelligent sensing and automation systems for precision agriculture, integrating AI-driven computer vision, autonomous robotics, and machine learning to detect crop-diseases, pests, and weeds, to monitor plant health, and to enable targeted interventions such as precision spraying. His work focuses on real-time crop scouting, pest and disease management, and data-informed decision support to improve farming efficiency and sustainability.

The graduate programs offered at U of G play a central role in educating and preparing the workforce for AI developments such as computer vision. The M.Sc. and PhD thesis-based graduate programs in Computer Science can provide research-intensive training in theoretical and applied AI, allowing graduate students to deepen their expertise in machine learning and related fields. The University also offers a Collaborative Specialization in Artificial Intelligence. 

The Master of Cybersecurity and Threat Intelligence (MCTI), and the newer offering of Master of Cybersecurity Leadership and Cyberpreneurship (MCLC), recognize that AI systems increasingly intersect with security, privacy, and governance concerns. These targeted programs help prepare graduates to manage the security challenges that accompany AI adoption.

As AI adoption expands across Canadian businesses and institutions, computer vision stands out as a technically demanding yet economically impactful capability, with universities like the University of Guelph advancing both the research and the highly trained graduates needed to support its growth.