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Shift Invariant Support Vector Machine for Image Classification in Automatic Target Recognition Systems.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Shift Invariant Support Vector Machine for Image Classification in Automatic Target Recognition Systems./
Author:	Omoregbee, Ehimwenma.
Description:	1 online resource (182 pages)
Notes:	Source: Masters Abstracts International, Volume: 84-11.
Contained By:	Masters Abstracts International84-11.
Subject:	Electrical engineering. -
Online resource:	click for full text (PQDT)
ISBN:	9798379572990

Shift Invariant Support Vector Machine for Image Classification in Automatic Target Recognition Systems.
Omoregbee, Ehimwenma.

Shift Invariant Support Vector Machine for Image Classification in Automatic Target Recognition Systems. - 1 online resource (182 pages)

Source: Masters Abstracts International, Volume: 84-11.

Thesis (M.S.)--Tuskegee University, 2023.

Includes bibliographical references

Detection and classification of Synthetic Aperture Radar (SAR) targets are especially important in automatic target recognition (ATR) applications. ATR includes detecting, classifying, and identifying targets within a scene. There are several approaches to ATR, and expectedly, each class of algorithms has strengths and also, exhibits some weaknesses. In this thesis, the focus is on developing a novel algorithm that combines the concepts of distance classifier correlation filters(DCCF) and Support vector machines (SVMs) to achieve the best possible shift-invariant classification tailored to real-time scenarios. The DCCF framework will be used as the kernel of the SVM algorithm; that is, we use DCCF to develop a new kernel function to make the non-linearly separable input data separable. We will demonstrate that the proposed kernel satisfies Mercer's condition, a theoretical requirement for viable SVM kernels.Although not shift-invariant, SVM algorithms are well-known for classification and tend to generalize well for targets not contained in the training set. DCCF increases the distance of separation between classes while making each class more compact by minimizing the intra-class distance and maximizing the inter-class distance. The proposed algorithm will rely on the strengths of DCCF and SVM algorithms. Performance results are assessed by comparing the proposed algorithm to state-of-the-art shift invariant ATR algorithms such as Maximum margin correlation filters (MMCF), Unconstrained Minimum average correlation energy (UMACE), and Optimum tradeoff synthetic discriminant function (OTSDF).

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798379572990Subjects--Topical Terms:

596380
Electrical engineering.
Subjects--Index Terms:

Synthetic aperture radarIndex Terms--Genre/Form:

554714
Electronic books.

Shift Invariant Support Vector Machine for Image Classification in Automatic Target Recognition Systems.
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500 $a Source: Masters Abstracts International, Volume: 84-11.
500 $a Advisor: Ndoye, Mandoye;Khan, Jesmin.
502 $a Thesis (M.S.)--Tuskegee University, 2023.
504 $a Includes bibliographical references
520 $a Detection and classification of Synthetic Aperture Radar (SAR) targets are especially important in automatic target recognition (ATR) applications. ATR includes detecting, classifying, and identifying targets within a scene. There are several approaches to ATR, and expectedly, each class of algorithms has strengths and also, exhibits some weaknesses. In this thesis, the focus is on developing a novel algorithm that combines the concepts of distance classifier correlation filters(DCCF) and Support vector machines (SVMs) to achieve the best possible shift-invariant classification tailored to real-time scenarios. The DCCF framework will be used as the kernel of the SVM algorithm; that is, we use DCCF to develop a new kernel function to make the non-linearly separable input data separable. We will demonstrate that the proposed kernel satisfies Mercer's condition, a theoretical requirement for viable SVM kernels.Although not shift-invariant, SVM algorithms are well-known for classification and tend to generalize well for targets not contained in the training set. DCCF increases the distance of separation between classes while making each class more compact by minimizing the intra-class distance and maximizing the inter-class distance. The proposed algorithm will rely on the strengths of DCCF and SVM algorithms. Performance results are assessed by comparing the proposed algorithm to state-of-the-art shift invariant ATR algorithms such as Maximum margin correlation filters (MMCF), Unconstrained Minimum average correlation energy (UMACE), and Optimum tradeoff synthetic discriminant function (OTSDF).
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Electrical engineering. $3 596380
650 4 $a Remote sensing. $3 557272
650 4 $a Computer science. $3 573171
653 $a Synthetic aperture radar
653 $a SVM algorithms
653 $a Support vector machines
653 $a Automatic target recognition
653 $a Input data
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690 $a 0984
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710 2 $a Tuskegee University. $b College of Engineering. $3 1468307
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