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Image feature detection and matching...

Deng, Hongli.

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Image feature detection and matching for biological object recognition.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Image feature detection and matching for biological object recognition./
作者:	Deng, Hongli.
面頁冊數:	163 p.
附註:	Source: Dissertation Abstracts International, Volume: 68-10, Section: B, page: 6754.
Contained By:	Dissertation Abstracts International68-10B.
標題:	Artificial Intelligence. -
電子資源:	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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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.
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