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52
AUTONOMOUS SHIP RECOGNITION FROM COLOR IMAGES
by
Deniz Kumlu
A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
MASTER OF SCIENCE
(ELECTRICAL ENGINEERING)
August 2012
Copyright 2012 Deniz Kumlu
Object Description
| Title | Autonomous ship recognition from color images |
| Author | Kumlu, Deniz |
| Author email | kumlu@usc.edu;dkumlu@gmail.com |
| Degree | Master of Science |
| Document type | Thesis |
| Degree program | Electrical Engineering |
| School | Viterbi School of Engineering |
| Date defended/completed | 2012-06-19 |
| Date submitted | 2012-07-25 |
| Date approved | 2012-07-25 |
| Restricted until | 2012-07-25 |
| Date published | 2012-07-25 |
| Advisor (committee chair) | Jenkins, Brian Keith |
| Advisor (committee member) |
Kuo, C. Jay Mendel, Jerry M. |
| Abstract | Autonomous ship recognition is an active area for military and commercial applications like harbor surveillance. Accurate identification of unknown contacts is critical in military intelligence. This automated system can help controllers to identify the point of contacts more quickly and accurately. This work mainly focuses on color images attained using digital cameras mounted on ships and harbors. Aside from using digital images for recognition, other information known are distance and course information attained from RADAR. For extracting significant features, spatial pyramid histogram technique is performed on a segmented ship image and support vector machines are used as a classifier. These particular data-sets contain 9 different types of ship with 18 different camera angle perspectives for training set, development set and testing set. The training data-set contains modeled synthetic images; development and testing data-sets contain real images. This work reports two experimental results for ship classification from color images. Our first experiment is based on classification of a synthetic image data-set versus real image data-set, which means the classifier is trained on the synthetic data-set and tested on the real data-set and obtained accuracy is 87.8%. Our second experiment is based on classification of synthetic images + real images (combined data-set) versus real images, which means the classifier is trained on the combined data-set and tested on a separate real data-set, and obtained accuracy is 93.3%. |
| Keyword | object recognition; ship recognition; image processing; Hough transform; connected component analysis; segmentation; feature extraction; spatial pyramid; random projection; support vector machines |
| Language | English |
| Part of collection | University of Southern California dissertations and theses |
| Publisher (of the original version) | University of Southern California |
| Place of publication (of the original version) | Los Angeles, California |
| Publisher (of the digital version) | University of Southern California. Libraries |
| Provenance | Electronically uploaded by the author |
| Type | texts |
| Legacy record ID | usctheses-m |
| Contributing entity | University of Southern California |
| Rights | Kumlu, Deniz |
| Physical access | The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the author, as the original true and official version of the work, but does not grant the reader permission to use the work if the desired use is covered by copyright. It is the author, as rights holder, who must provide use permission if such use is covered by copyright. The original signature page accompanying the original submission of the work to the USC Libraries is retained by the USC Libraries and a copy of it may be obtained by authorized requesters contacting the repository e-mail address given. |
| Repository name | University of Southern California Digital Library |
| Repository address | USC Digital Library, University of Southern California, University Park Campus MC 7002, 106 University Village, Los Angeles, California 90089-7002, USA |
| Repository email | cisadmin@dots.usc.edu |
| Archival file | uscthesesreloadpub_Volume4/etd-KumluDeniz-979.pdf |
Description
| Title | Page 1 |
| Contributing entity | University of Southern California |
| Repository email | cisadmin@dots.usc.edu |
| Full text | 52 AUTONOMOUS SHIP RECOGNITION FROM COLOR IMAGES by Deniz Kumlu A Dissertation Presented to the FACULTY OF THE USC GRADUATE SCHOOL UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Fulfillment of the Requirements for the Degree MASTER OF SCIENCE (ELECTRICAL ENGINEERING) August 2012 Copyright 2012 Deniz Kumlu |
