Contour entropy decision directed adaptive filters

Abstract

Many previously defined nonlinear adaptive filters require the use of manual thresholds, and therefore, user interaction. During implementation of such filters, the user must continuously alter the threshold values and view each resulting image, until the best filtered image is produced. This long and tedious process prompts the search for an automated system, requiring limited user interface. One such system, developed as the main emphasis of this thesis, is an automatic entropy based nonlinear adaptive filter. Entropy, in this context, refers to the information content accumulated along the contours of an image. Image regions yielding high entropy contours are shown to contain important image features necessary for perception. By using this concept of contour entropy, the need for a manual binary threshold is eliminated and an autonomous system is created. Several intermediate filters are created during the developmental phase of the contour entropy based filter. The main focus of all these filters is to distinguish between background, and image feature regions. Attenuating noise, while preserving edge sharpness and image detail is the ultimate goal of all the filters implemented.

Notes

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Graduation Date

1992

Semester

Spring

Advisor

Weeks, Arthur R.

Degree

Master of Science (M.S.)

College

College of Engineering

Department

Computer Engineering

Format

PDF

Pages

256 p.

Language

English

Length of Campus-only Access

None

Access Status

Masters Thesis (Open Access)

Identifier

DP0029865

Subjects

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

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