Machine-driven analysis of visual data is the hard core of intelligent surveillance
systems. Its main goal is to recognize di erent objects in the video sequence and their
behaviour. Such operation is very challenging due to the dynamic nature of the scene
and the lack of semantic-comprehension for visual data in machines. The general
ow
of the recognition process starts with the object extraction task. For so long, this task
has been performed using image segmentation. However, recent years have seen the
emergence of another contender, image matting. As a well-known process, matting
has a very rich literature, most of which is designated to interactive approaches for
applications like movie editing. Thus, it was conventionally not considered for visual
data analysis operations.
Following the new shift toward matting as a means to object extraction, two methods
have stood out for their foreground-extraction accuracy and, more importantly,
their automation potential. These methods are Closed-Form Matting (CFM) and
Spectral Matting (SM). They pose the matting process as either a constrained optimization
problem or a segmentation-like component selection process. This di erence
of formulation stems from an interesting di erence of perspective on the matting process,
opening the door for more automation possibilities. Consequently, both of these
methods have been the subject of some automation attempts that produced some intriguing results.
For their importance and potential, this thesis will provide detailed discussion and
analysis on two of the most successful techniques proposed to automate the CFM and
SM methods. In the beginning, focus will be on introducing the theoretical grounds
of both matting methods as well as the automatic techniques. Then, it will be shifted
toward a full analysis and assessment of the performance and implementation of these
automation attempts. To conclude the thesis, a brief discussion on possible improvements
will be presented, within which a hybrid technique is proposed to combine the
best features of the reviewed two techniques. / Thesis / Master of Applied Science (MASc)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/18661 |
| Date | 16 December 2015 |
| Creators | Alrabeiah, Muhammad |
| Contributors | Chen, Jun, Electrical and Computer Engineering |
| Source Sets | McMaster University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis |
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