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Towards a data-driven object recognition framework using temporal depth-data


Towards a data-driven object recognition framework using temporal depth-data

Birkas, David (2015) Towards a data-driven object recognition framework using temporal depth-data. Masters thesis, Concordia University.

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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
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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