Birkas, David (2015) Towards a data-driven object recognition framework using temporal depth-data. Masters thesis, Concordia University.
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Abstract
Object recognition using depth-sensors such as the Kinect device has received a lot of attention in recent years. Yet the limitations of such devices such as large noise and missing data makes the problem very challenging. In this work I propose a framework for data-driven object recognition that uses a combination of local and global features as well as time varying depth information.
Divisions: | Concordia University > Gina Cody School of Engineering and Computer Science |
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Item Type: | Thesis (Masters) |
Authors: | Birkas, David |
Institution: | Concordia University |
Degree Name: | M. Comp. Sc. |
Program: | Computer Science |
Date: | 15 December 2015 |
Thesis Supervisor(s): | Popa, Tiberiu |
Keywords: | Object detection, Object retrieval, Sketch-based object detection, Sketch-based object retrieval |
ID Code: | 980842 |
Deposited By: | DAVID BIRKAS |
Deposited On: | 16 Jun 2016 14:32 |
Last Modified: | 18 Jan 2018 17:52 |
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