Integrating bottom-up and top-down information

In this thesis I present a framework for integrating bottom-up and top-down computer vision algorithms. I developed this framework, which I call the Map-Dictionary Pixel framework, because my intuition is that there is a need for tools that make it easier to build computer vision systems that mimic the way human visual systems process information. In particular, we humans humans create models of objects around us, and we use these models, top-down, to interpret, analyze and discern objects in the information that comes bottom-up from the visual world. After introducing my Map-Dictionary Pixel framework, I demonstrate how it empowers computer vision algorithms. I implement two different systems that extract the pixels of the image that correspond to a human. Even though each system uses different sets of algorithms, both use Map-Dictionary Pixel framework as the connecting pipeline. The two implementations demonstrate the utility of the Map-Dictionary Pixel framework and provide an example of how it can be used.

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2014

Author dc:creator

• Giacaglia, Giuliano Pezzolo

Advisor dc:contributor.advisor

• Patrick Henry Winston.

• Electrical Engineering and Computer Science.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/91813

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/91813