Detection, Segmentation, and Pose Recognition of Hands in Images

Abstract

Hand detection, segmentation, and pose recognition are challenging problems in Computer Vision with a wide variety of potential applications like alternative input devices, surveillance, motion capture, and augmented reality. This work proposes methods to solve each of these problems in high-resolution, monochromatic images via shape and texture-based methods. Hand Detection and Segmentation: This method of hand detection is based upon the outputs from both line-finding and curve-finding algorithms to find shapes that appear to be finger-like. A series of tests is performed on each finger candidate to further remove false positives and determine which sets of them could possibly form a human hand. Pose Recognition: Pose recognition works on database model, taking as input both a test image and a database of all possible hand poses. By using a scoring system comprised of votes between two different distance measures, the algorithm returns a list of database images in order of similarity to the test image. Pose Detection in a Video: To determine if a given hand pose occurs in the frames of a video sequence, the algorithm performs the pose recognition method described above, but inputs the pose to look for as the test image and the video sequence as the "database." It then returns a list of frames in the sequence in order of similarity to the given pose.

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

DP0022104

Language

English

Access Status

Open Access

Length of Campus-only Access

None

Document Type

Honors in the Major Thesis

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