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Visual Context Classification for an Autonomous Robot

Quispel, L. (1998) Visual Context Classification for an Autonomous Robot. Master's Thesis / Essay, Artificial Intelligence.

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A system is described that is able to classify visual contexts. It can be used in a supporting role, to help the navigation of a robot, help object recognition, or to impose behavioral constraints. A visual context is taken to be the visually perceived environment. Because the system is to be distance independent, image pyramids are used. The system is to be applicable to a wide range of possible visual contexts. Therefore, no specific information about the scene or task is used. Instead, autocorrelation functions of the various scales of the pyramid are calculated and added. The resulting feature vectors are classified using a linear Baysian classifier. The system can function supervised, with a set of pretrained classes, as well as unsupervised, making its own classes at runtime. It is integrated with the behavioral architecture of an autonomous robot. The use of the system and the architecture is discussed. Also, the system is tested on a set of different tasks, both with pretaken images and in real time tasks. The advantages and disadvantages of the system are discussed.

Item Type: Thesis (Master's Thesis / Essay)
Degree programme: Artificial Intelligence
Thesis type: Master's Thesis / Essay
Language: English
Date Deposited: 15 Feb 2018 07:30
Last Modified: 15 Feb 2018 07:30

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