Skip to main content
Ontario Tech acknowledges the lands and people of the Mississaugas of Scugog Island First Nation.

We are thankful to be welcome on these lands in friendship. The lands we are situated on are covered by the Williams Treaties and are the traditional territory of the Mississaugas, a branch of the greater Anishinaabeg Nation, including Algonquin, Ojibway, Odawa and Pottawatomi. These lands remain home to many Indigenous nations and peoples.

We acknowledge this land out of respect for the Indigenous nations who have cared for Turtle Island, also called North America, from before the arrival of settler peoples until this day. Most importantly, we acknowledge that the history of these lands has been tainted by poor treatment and a lack of friendship with the First Nations who call them home.

This history is something we are all affected by because we are all treaty people in Canada. We all have a shared history to reflect on, and each of us is affected by this history in different ways. Our past defines our present, but if we move forward as friends and allies, then it does not have to define our future.

Learn more about Indigenous Education and Cultural Services

September 21, 2011

Speaker: Dr. Michael Greenspan, Dept. of Electrical and Computer Engineering, Queen's University.

Title: Object Recognition in Range Data

Abstract: In this talk, I will review the progress that my students and I have made since 2001 in the field of range image vision. The motivation of our research has been the automatic robotic grasping of free-flying satellites. This application is particularly challenging due to the sparseness of the data that is acquired by space-qualified range vision sensors, which may comprise only thousands or hundreds of points per frame. I will focus on two model-based algorithms for pose determination and object recognition in range data. One is a local method, called Variable-Dimensional Local Shape Descriptors, that automatically selects the best feature properties, using a training phase. The second is a global method, called Potential Well Space Embedding, that exploits the existence of local minima of the Iterative Closest Point potential well space. I will describe and compare these two algorithms, and describe future research directions, as well as a number of interesting applications. 

Biography: Michael Greenspan is a Professor within and Head of the Department of Electrical and Computer Engineering, with a cross-appointment to the School of Computing at Queen's University, Kingston, Canada. His research interests include pose determination, object recognition, and tracking (especially using range data), and robotic gaming systems. Professor Greenspan was the recipient of the Canadian Image Processing and Pattern Recognition 2003 Young Investigators Award, and the Premier's Research Excellence Award.