Adapting Efficient Subwindow Search for Embedded System Applications
| Category | Publications |
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| Abstract | As a site-placed IREE 2010 Awardee, I spent eleven weeks at Intel China Research Center in Beijing adapting the state-of-the-art Efficient Subwindow Search (ESS)1 computer vision algorithm for its future use in Intel embedded system software. This required an investigation of current novel machine learning algorithms necessary to run ESS on raw image data. Through the collaboration, I provided new evaluation code, modified code for feature extraction, and an overarching package that will allow a user to perform ESS on any raw input image given a set of training examples. This software may find use in Advanced Driver Automated Security (ADAS) systems or other object recognition applications. |
| Contributor | Mourad Ouzzani
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| Bio | Andrew Head is currently studying for a bachelor’s of science degree in Computer Engineering at the University of Pittsburgh. During the last year of his study, he has explored his interest in computer programming at the University’s Vibrations and Control Laboratory, and now at the Intel China Research Center in Beijing. His work so far has focused on machine learning and computer vision techniques, although he is now looking toward applications that will combine his love for both programming and electronic music. |
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