Visual Models - Application to Situational Awareness

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My research addresses the problem of representations for computer vision, specifically how to capture information from a sensor network and create computer models that faithfully reflect an environment over time. There are 3 specific sub-projects within this proposal. The first deals with providing situational awareness for a robotic assistant working in conjunction with a human. Objects (humans + machines) are represented as articulated 3D models that track their real world counterparts in real time. The scientific focus is how to capture and model dynamic behavior so that the system can identify or control the activities of each occupant. Following on previous work with GM research, the goal is to embed robotic assistants with more powerful and reliable situational awareness so that practical implementations become possible. If this vision is successful, future assembly lines will be comprised of human-robot teams collaborating to manufacture products, greatly scaling up productivity and allowing Canada to remain competitive in the global marketplace.******The remaining 2 sub-projects are aimed at representations that enable humans to have more precise knowledge about their environments, and robots to be able to function within them. In the first, Sparse Data Models, we look at the problem of how to recover descriptions of an environment with limited sensory ability. The specific example is localizing underground mineral deposits from physical core samples, which are very sparse relative to what one finds in images. We recently have developed some new stochastic modeling techniques that show promise for extending conventional image reconstruction algorithms to these challenging datasets. If successful, this research will lead to more precise algorithms for localizing mineral deposits, which in turn could have a significant impact on mining operations by reducing costs for excavation, transport and processing.******Finally, the Deep Learning sub-project is an attempt to leverage impressive technical progress in machine learning to determine representations that are better suited to natural forms. Our focus is on the practical implementation of Hierarchical Generative Models using Convolutional Deep Boltzman machine networks, with the goal of achieving comparable performance with a substantial reduction in complexity. This research will contribute to work with GM in building sensor systems that can operate in off-road environments under adverse weather conditions, as well as resource industry projects that involve identifying structures and landmarks in GPS-deprived environments.*****