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|a eng
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| 100 |
1 |
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|a Zhang, Huigang
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Object detection via structural feature selection and shape model
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| 264 |
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|c 2013
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| 336 |
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
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|2 rdamedia
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| 338 |
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|a ƒa Online-Ressource
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|2 rdacarrier
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| 500 |
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|a Date Completed 16.05.2014
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| 500 |
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|a Date Revised 14.11.2013
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| 500 |
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|a published: Print
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| 500 |
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|a Citation Status PubMed-not-MEDLINE
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| 520 |
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|a In this paper, we propose an approach for object detection via structural feature selection and part-based shape model. It automatically learns a shape model from cluttered training images without need to explicitly use bounding boxes on objects. Our approach first builds a class-specific codebook of local contour features, and then generates structural feature descriptors by combining context shape information. These descriptors are robust to both within-class variations and scale changes. Through exploring pairwise image matching using fast earth mover's distance, feature weights can be iteratively updated. Those discriminative foreground features are assigned high weights and then selected to build a part-based shape model. Finally, object detection is performed by matching each testing image with this model. Experiments show that the proposed method is very effective. It has achieved comparable performance to the state-of-the-art shape-based detection methods, but requires much less training information
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| 650 |
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4 |
|a Journal Article
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| 650 |
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4 |
|a Research Support, Non-U.S. Gov't
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| 700 |
1 |
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|a Bai, Xiao
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhou, Jun
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Cheng, Jian
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhao, Huijie
|e verfasserin
|4 aut
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| 773 |
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8 |
|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 22(2013), 12 vom: 16. Dez., Seite 4984-95
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|g year:2013
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|g month:12
|g pages:4984-95
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