Three dimensional object recognition

Abstract

Object recognition is a complex problem in computer vision. In most recognition systems, features are extracted from sensors such as intensity, tactile, and range. These features are matched to a database of modeled objects in an attempt to determine which object(s) are present. Once the object identities are known, the orientation of each object relative to some base frame of reference is determined. A solution for recognizing polyhedral objects using surface normals as the sole input feature is given. This technique exploits strong constraints on the angles between the faces of an object to perform recognition. Exhaustive experiments involving the of use of all possible combinations of input to the system have yielded encouraging results. The system mentioned uses only surface normals which could be derived from a single sensor. However, multiple sensors can be used to make recognition easier by providing different features. The addition of multiple sensors introduces the problem of combining features which are sometimes contradictory. A Bayesian technique for fusing data between sensors, of any type is proposed. This method adjusts the confidence we have in each feature based on the support from multiple sensors. The confidence values aid in the elimination of noisy input to a recognition system making those systems more robust.

Notes

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Thesis Completion

1991

Semester

Spring

Advisor

Shah, Mubarak

Degree

Bachelor of Science (B.S.)

College

College of Arts and Sciences

Degree Program

Computer Science

Subjects

Arts and Sciences -- Dissertations, Academic;Dissertations, Academic -- Arts and Sciences

Format

Print

Identifier

DP0020831

Language

English

Access Status

Open Access

Length of Campus-only Access

None

Document Type

Honors in the Major Thesis

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