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Image feature detection and matching...

Deng, Hongli.

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Image feature detection and matching for biological object recognition.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Image feature detection and matching for biological object recognition./
Author:	Deng, Hongli.
Description:	163 p.
Notes:	Source: Dissertation Abstracts International, Volume: 68-10, Section: B, page: 6754.
Contained By:	Dissertation Abstracts International68-10B.
Subject:	Artificial Intelligence. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3286036
ISBN:	9780549284536

Image feature detection and matching for biological object recognition.
Deng, Hongli.

Image feature detection and matching for biological object recognition. - 163 p.

Source: Dissertation Abstracts International, Volume: 68-10, Section: B, page: 6754.

Thesis (Ph.D.)--Oregon State University, 2007.

Image feature detection and matching are two critical processes for many computer vision tasks. Currently, intensity-based local interest region detectors and local feature-based matching methods are used widely in computer vision applications. But in some applications, such as biological object recognition tasks, within-class changes in pose, lighting, color, and texture can cause considerable variation of local intensity. Consequently, object recognition systems based on intensity-based interest region detectors often fail. This dissertation proposes a new structure-based local interest region detector called principal curvature-based region detector (PCBR) that detects stable watershed regions within the multi-scale principal curvature images. This detector typically detects distinctive patterns distributed evenly on the objects and it shows significant robustness to local intensity perturbation and intra-class variation. Second, this thesis develops a local feature matching algorithm that augments the SIFT descriptor with a global context feature vector containing curvilinear shape information from a much larger neighborhood to resolve ambiguity in matching. Moreover, this thesis further improves the matching method to make it robust to occlusion, clutter, and non-rigid transformation by defining affine-invariant log-polar elliptical context and employing a reinforcement matching scheme. Results show that our new detector and matching algorithms improve recognition accuracy and are well suited for biological object recognition tasks.

ISBN: 9780549284536Subjects--Topical Terms:

769149
Artificial Intelligence.

Image feature detection and matching for biological object recognition.
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020 $a 9780549284536
035 $a (UMI)AAI3286036
035 $a AAI3286036
040 $a UMI $c UMI
100 1 $a Deng, Hongli. $3 1282422
245 1 0 $a Image feature detection and matching for biological object recognition.
300 $a 163 p.
500 $a Source: Dissertation Abstracts International, Volume: 68-10, Section: B, page: 6754.
502 $a Thesis (Ph.D.)--Oregon State University, 2007.
520 $a Image feature detection and matching are two critical processes for many computer vision tasks. Currently, intensity-based local interest region detectors and local feature-based matching methods are used widely in computer vision applications. But in some applications, such as biological object recognition tasks, within-class changes in pose, lighting, color, and texture can cause considerable variation of local intensity. Consequently, object recognition systems based on intensity-based interest region detectors often fail. This dissertation proposes a new structure-based local interest region detector called principal curvature-based region detector (PCBR) that detects stable watershed regions within the multi-scale principal curvature images. This detector typically detects distinctive patterns distributed evenly on the objects and it shows significant robustness to local intensity perturbation and intra-class variation. Second, this thesis develops a local feature matching algorithm that augments the SIFT descriptor with a global context feature vector containing curvilinear shape information from a much larger neighborhood to resolve ambiguity in matching. Moreover, this thesis further improves the matching method to make it robust to occlusion, clutter, and non-rigid transformation by defining affine-invariant log-polar elliptical context and employing a reinforcement matching scheme. Results show that our new detector and matching algorithms improve recognition accuracy and are well suited for biological object recognition tasks.
590 $a School code: 0172.
650 4 $a Artificial Intelligence. $3 769149
650 4 $a Biology, Entomology. $3 1018619
650 4 $a Computer Science. $3 626642
690 $a 0353
690 $a 0800
690 $a 0984
710 2 $a Oregon State University. $3 625720
773 0 $t Dissertation Abstracts International $g 68-10B.
790 $a 0172
791 $a Ph.D.
792 $a 2007
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3286036