Scale Space Based Grammar for Hand Detection
For detecting difficult objects, such as hands, an algorithm is presented that uses tokens and a grammar. Tokens are found by employing a new scale space edge detector that finds scale invariant features at object boundaries. First, the scale space is constructed. Then edges at each scale are found and the scale space is flattened into a single edge image. To detect a hand pattern, a grammar is defined using curve tokens for finger tips and wedges, and line tokens for finger sides. Curve tokens are found by superimposing a curve model on the scale space edge image and scoring its fit. Line tokens are found by using a modified Burns line finder. A hand pattern is identified by parsing these tokens using a graph based algorithm. On a database of 200 images of finger tips and wedges, finger tip curves are detected 85% of the time, and wedge curves are detected 70% of the time. On a database of 287 images of open hands against cluttered backgrounds, hands are correctly identified 70% of the time.
This item is only available in print in the UCF Libraries. If this is your thesis or dissertation, you can help us make it available online for use by researchers around the world by downloading and filling out the Internet Distribution Consent Agreement. You may also contact the project coordinator Kerri Bottorff for more information.
daVitoria Lobo, Niels J.
Bachelor of Science (B.S.)
College of Engineering and Computer Science
Dissertations, Academic -- Engineering; Engineering -- Dissertations, Academic
Length of Campus-only Access
Honors in the Major Thesis
Prokaj, Jan, "Scale Space Based Grammar for Hand Detection" (2006). HIM 1990-2015. 549.