Computer and Robot Vision Research

One of my areas of research has been computer vision for robotic applications. Computer vision is not only a fascinating field of study, but it has a numerous beneficial applications. Vision is a rich sensing modality capable of providing detailed feedback suitable for monitoring and control, particularly useful in unstructured environments. Computer vision equips robots with the sensory feedback necessary to help automate the drudgery of repetitive tasks. It has applications in product inspection, material sorting, quality assurance, and assembly. It has important environmental applications such as satellite remote sensing and environmental monitoring. Computer vision helps robots operate reliably in situations where it is not practical or safe for a human to be present including hostile environments such as humanitarian de-mining, hazardous waste clean-up, and space exploration. Computer vision can also serve human well-being in applications like lane-departure warning systems, collision avoidance, medical imaging, and search and rescue. Computer vision has vast opportunities to be directed towards the public good.

One area of my research has focused on computer vision using machine learning (ML). For example, subspace vision techniques operate by “learning” the appearance or position of an object and therefore do not rely on having predefined models. Another approach to machine learning is support vector machines (SVMs) which can also be applied to computer vision. Some of my research has explored the use of SVMs for classifying objects or obstacles in an image. Given a data set, an SVM attempts to identify a hyperplane which separates different classes of data. The hyperplane is then used as a classification boundary to predict which class a data point belongs. The SVM provides a powerful tool for pattern recognition in a variety of computer vision applications. One application which has been explored in our research is the use of SVMs to identify obstacles for mobile robots. Another area which has been explored is the application of SVMs for the visual sorting of recyclable goods, an example of computer vision being directed towards environmental sustainability and eliminating the drudgery of hand-sorting.

Robot in the hallway Other research has explored the use of FPGAs (Field Programmable Gate Arrays) for computer vision. The large amount of data and computations in computer vision applications can present a challenge for conventional microcomputers. Performing real-time image processing on a typical video signal can be likened to "drinking from a firehose". FPGAs can exploit the parallelism found in computer vision tasks much better than conventional CPUs. FPGAs can be readily designed with custom parallel digital circuitry tailored for performing various image processing tasks making them well-suited for high-speed, real-time vision processing systems.

Robot grabbing my tie Another area of interest is exploring GPUs (Graphical Processing Units), whose highly parallel structure makes them effective for certain computer vision tasks. For example, the CUDA (Compute Unified Device Architecture) is a parallel computing architecture developed by NVidia that can be programmed to executed numerous threads simultaneously. Architectures like CUDA are attractive for tackling highly parallel computer vision tasks that require real-time performance.

Robot in the lab Most of my research would not be possible without the help of capable students. Some of selected publications are listed below (^* denotes student researchers):

Bryan W. House^*, David W. Capson, Derek C. Schuurman, "Towards Real-Time Sorting of Recyclable Goods Using Support Vector Machines", Proceedings of the IEEE International Symposium on Sustainable Systems and Technology (ISSST), May 2011.
Michael Nawrocky^*, Derek C. Schuurman, and Jeff Fortuna, "Visual Sorting of Recyclable Goods Using a Support Vector Machine", Proceedings of the IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), May 2010.
Timothy Ubbens^* and Derek C. Schuurman, "Vision-based Obstacle Detection Using a Support Vector Machine", Proceedings of the IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) May 2009, pp. 459-462.
Fang Zhong^*, David W. Capson, Derek C. Schuurman, "Parallel Architecture for PCA Image Feature Detection Using FPGA", Proceedings of the IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), May 2008, pp. 1341-1344.
A. Smit^* and D.C. Schuurman, "Robust Subspace Position Measurement Using Localized Sub-Windows", Proceedings of the Canadian Conference on Computer and Robot Vision, May 2007, pp. 282-288.
D.C. Schuurman and D.W. Capson, “Robust Direct Visual Servoing Using Network-Synchronized Cameras”, IEEE Transactions on Robotics and Automation, Volume 20, Issue 2, April 2004, pp. 319-334.
D.C. Schuurman, D.W. Capson, “Real-Time Synchronized Vision Sensors Over Ethernet”, Proceedings of the IEEE International Conference on Local Computer Networks, Tampa, Florida, November 2004, pp. 136-143.
J. Fortuna, D.C. Schuurman, D.W. Capson, “A Comparison of PCA and ICA for Object Recognition Under Varying Illumination”, Proceedings of the International Conference on Pattern Recognition, Quebec City, PQ, August 2002, Vol. 3, pp. 11-15.
D.C. Schuurman and D.W. Capson, “Direct Visual Servoing Using Network-Synchronized Cameras and Kalman Filter”, Proceedings of the IEEE International Conference on Robotics and Automation, Washington DC, May 2002, pp. 4191-4197.
D.C. Schuurman and D.W. Capson, “Video-Rate Eigenspace Methods for Position Tracking and Remote Monitoring”, Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation, Santa Fe, NM, April 2002, pp. 45-49.

Some EV and power electronics publications

For a time, my research and work focused on power electronics for use in electric vehicle (EV) applications (before it was cool). My work focused mostly on the design of embedded systems and power electronics for traction drive systems. Here are a few publications related to that work:

D.C. Schuurman and R.S. Ramshaw, “Dynamic Simulation of an EV Traction Application”, Proceedings of the IASTED International Conference on High Technology in the Power Industry, June 1996, pp. 66-69.
R.S. Ramshaw and D.C. Schuurman, PSpice Simulation of Power-Electronics Circuits, Springer, 1997. (companion website)
US 5874818: Method and Apparatus For Sensing Load Current in a Motor Controller
US 6215681: Bus Bar Heat Sink. See also: CA 2325148 (Canada), EP 1100294 (Europe), JP 2001267479 (Japan)