Scale Space Based Grammar for Hand Detection

Abstract

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.

Notes

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Thesis Completion

2006

Semester

Spring

Advisor

daVitoria Lobo, Niels J.

Degree

Bachelor of Science (B.S.)

College

College of Engineering and Computer Science

Degree Program

Computer Science

Subjects

Dissertations, Academic -- Engineering; Engineering -- Dissertations, Academic

Format

Print

Identifier

DP0022097

Language

English

Access Status

Open Access

Length of Campus-only Access

None

Document Type

Honors in the Major Thesis

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