This thesis proposes an algorithm for automatically classifying a specific set of image quality (IQ) defects on noisy and mottled printed documents. A rough initial estimate of the defects' location i.e. defect detection is manually provided with a scanned image. The approach then proceeds to derive a more accurate segmentation by performing several image processing routines on the digital image. This reveals regions of interest from which discriminatory features are extracted. A classification of four defects is achieved via a customized tree classifier which employs a combination of size, shape and region attributes at corresponding nodes to yield appropriate binary decisions. Applications of this process include automated/assisted diagnosis and repair of printers or copiers in the field in a timely fashion. The proposed algorithm is tested on a database of 261 scanned images provided by Xerox Corporation and several synthetic defects yielding a 96.6% classification rate.

Library of Congress Subject Headings

Image processing; Imaging systems--Image quality; Printing--Data processing; Algorithms

Publication Date


Document Type


Student Type


Degree Name

Electrical Engineering (MS)

Department, Program, or Center

Electrical Engineering (KGCOE)


Eli Saber

Advisor/Committee Member

Sohail Dianat

Advisor/Committee Member

Daniel Phillips


Physical copy available from RIT's Wallace Library at TA1637 .U42 2006


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