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Shift Invariant Support Vector Machi...

Omoregbee, Ehimwenma.

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

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Shift Invariant Support Vector Machine for Image Classification in Automatic Target Recognition Systems./
作者:	Omoregbee, Ehimwenma.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
面頁冊數:	182 p.
附註:	Source: Masters Abstracts International, Volume: 84-11.
Contained By:	Masters Abstracts International84-11.
標題:	Electrical engineering. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30423432
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. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 182 p.

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

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

This item must not be sold to any third party vendors.

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).

ISBN: 9798379572990Subjects--Topical Terms:

649834
Electrical engineering.
Subjects--Index Terms:

Synthetic aperture radar

Shift Invariant Support Vector Machine for Image Classification in Automatic Target Recognition Systems.
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300 $a 182 p.
500 $a Source: Masters Abstracts International, Volume: 84-11.
500 $a Advisor: Ndoye, Mandoye;Khan, Jesmin.
502 $a Thesis (M.S.)--Tuskegee University, 2023.
506 $a This item must not be sold to any third party vendors.
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).
590 $a School code: 1193.
650 4 $a Electrical engineering. $3 649834
650 4 $a Remote sensing. $3 535394
650 4 $a Computer science. $3 523869
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
690 $a 0799
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791 $a M.S.
792 $a 2023
793 $a English
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