Doctoral Dissertations

Soumya De

Abstract

"Data fusion is a technique for combining data obtained from multiple sources for an enhanced detection or decision. Fusion of data can be done at the raw-data level, feature level or decision level. Applications of data fusion include defense (such as battlefield surveillance and autonomous vehicle control), medical diagnosis and structural health monitoring. Techniques for data fusion have been drawn from areas such as statistics, image processing, pattern recognition and computational intelligence. This dissertation includes investigation and development of methods to perform data fusion for nondestructive evaluation (NDE) and medical imaging applications. The general framework for these applications includes region-of-interest (ROI) detection followed by feature extraction and classification of the detected ROI. Image processing methods such as edge detection and projection-based methods were used for ROI detection. The features extracted from the detected ROIs include texture, color, shape/geometry and profile-based correlation. Analysis and classification of the detected ROIs was performed using feature- and decision-level data fusion techniques such as fuzzy-logic, statistical methods and voting algorithms"--Abstract, page iv.

Advisor(s)

Stanley, R. Joe

Committee Member(s)

Moss, Randy Hays, 1953-
Stoecker, William V.
Xiao, Hai, Dr.
Adekpedjou, Akim

Department(s)

Electrical and Computer Engineering

Degree Name

Ph. D. in Electrical Engineering

Publisher

Missouri University of Science and Technology

Publication Date

2013

Journal article titles appearing in thesis/dissertation

  • A comprehensive structural analysis process for failure assessment in aircraft lap-joint mimics using intra-modal fusion of eddy current data
  • A comprehensive multi-modal NDE data fusion approach for failure assessment in aircraft lap-joint mimics
  • Automated biomedical text detection method in support of multi-scale content-based image retrieval systems
  • A data fusion-based approach for uterine cervical cancer histology image classification

Pagination

xiv, 187 pages

Note about bibliography

Includes bibliographical references.

Rights

© 2013 Soumya De, All rights reserved.

Document Type

Dissertation - Open Access

File Type

text

Language

English

Subject Headings

Multisensor data fusionNondestructive testingImage processing -- Analysis -- Technique

Thesis Number

T 10649

Print OCLC #

922573488

Electronic OCLC #

922573191

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