國立虎尾科技大學 |

Computer Vision and Deep Learning with Applications to Object Detection, Segmentation, and Document Analysis.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Computer Vision and Deep Learning with Applications to Object Detection, Segmentation, and Document Analysis./
作者:	Du, Xianzhi.
面頁冊數:	1 online resource (137 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-11(E), Section: B.
Contained By:	Dissertation Abstracts International79-11B(E).
標題:	Artificial intelligence. -
電子資源:	click for full text (PQDT)
ISBN:	9780438139770

Computer Vision and Deep Learning with Applications to Object Detection, Segmentation, and Document Analysis.
Du, Xianzhi.

Computer Vision and Deep Learning with Applications to Object Detection, Segmentation, and Document Analysis. - 1 online resource (137 pages)

Source: Dissertation Abstracts International, Volume: 79-11(E), Section: B.

Thesis (Ph.D.)--University of Maryland, College Park, 2017.

Includes bibliographical references

There are three work on signature matching for document analysis. In the first work, we propose a large-scale signature matching method based on locality sensitive hashing (LSH). Shape Context features are used to describe the structure of signatures. Two stages of hashing are performed to find the nearest neighbors for query signatures. We show that our algorithm can achieve a high accuracy even when few signatures are collected from one same person and perform fast matching when dealing with a large dataset. In the second work, we present a novel signature matching method based on supervised topic models. Shape Context features are extracted from signature shape contours which capture the local variations in signature properties. We then use the concept of topic models to learn the shape context features which correspond to individual authors. We demonstrate considerable improvement over state of the art methods. In the third work, we present a partial signature matching method using graphical models. In additional to the second work, modified shape context features are extracted from the contour of signatures to describe both full and partial signatures. Hierarchical Dirichlet processes are implemented to infer the number of salient regions needed. The results show the effectiveness of the approach for both the partial and full signature matching.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780438139770Subjects--Topical Terms:

559380
Artificial intelligence.
Index Terms--Genre/Form:

554714
Electronic books.

Computer Vision and Deep Learning with Applications to Object Detection, Segmentation, and Document Analysis.
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245 1 0 $a Computer Vision and Deep Learning with Applications to Object Detection, Segmentation, and Document Analysis.
264 0 $c 2017
300 $a 1 online resource (137 pages)
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500 $a Source: Dissertation Abstracts International, Volume: 79-11(E), Section: B.
500 $a Advisers: Larry Davis; David Doermann.
502 $a Thesis (Ph.D.)--University of Maryland, College Park, 2017.
504 $a Includes bibliographical references
520 $a There are three work on signature matching for document analysis. In the first work, we propose a large-scale signature matching method based on locality sensitive hashing (LSH). Shape Context features are used to describe the structure of signatures. Two stages of hashing are performed to find the nearest neighbors for query signatures. We show that our algorithm can achieve a high accuracy even when few signatures are collected from one same person and perform fast matching when dealing with a large dataset. In the second work, we present a novel signature matching method based on supervised topic models. Shape Context features are extracted from signature shape contours which capture the local variations in signature properties. We then use the concept of topic models to learn the shape context features which correspond to individual authors. We demonstrate considerable improvement over state of the art methods. In the third work, we present a partial signature matching method using graphical models. In additional to the second work, modified shape context features are extracted from the contour of signatures to describe both full and partial signatures. Hierarchical Dirichlet processes are implemented to infer the number of salient regions needed. The results show the effectiveness of the approach for both the partial and full signature matching.
520 $a There are three work on deep learning for object detection and segmentation. In the first work, we propose a deep neural network fusion architecture for fast and robust pedestrian detection. The proposed network fusion architecture allows for parallel processing of multiple networks for speed. A single shot deep convolutional network is trained as an object detector to generate all possible pedestrian candidates of different sizes and occlusions. Next, multiple deep neural networks are used in parallel for further refinement of these pedestrian candidates. We introduce a soft-rejection based network fusion method to fuse the soft metrics from all networks together to generate the final confidence scores. Our method performs better than existing state-of-the-arts, especially when detecting small-size and occluded pedestrians. Furthermore, we propose a method for integrating pixel-wise semantic segmentation network into the network fusion architecture as a reinforcement to the pedestrian detector. In the second work, in addition to the first work, a fusion network is trained to fuse the multiple classification networks. Furthermore, a novel soft-label method is devised to assign floating point labels to the pedestrian candidates. This metric for each candidate detection is derived from the percentage of overlap of its bounding box with those of other ground truth classes. In the third work, we propose a boundary-sensitive deep neural network architecture for portrait segmentation. A residual network and atrous convolution based framework is trained as the base portrait segmentation network. To better solve boundary segmentation, three techniques are introduced. First, an individual boundary-sensitive kernel is introduced by labeling the boundary pixels as a separate class and using the soft-label strategy to assign floating-point label vectors to pixels in the boundary class. Each pixel contributes to multiple classes when updating loss based on its relative position to the contour. Second, a global boundary-sensitive kernel is used when updating loss function to assign different weights to pixel locations on one image to constrain the global shape of the resulted segmentation map. Third, we add multiple binary classifiers to classify boundary-sensitive portrait attributes, so as to refine the learning process of our model.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Artificial intelligence. $3 559380
650 4 $a Computer science. $3 573171
655 7 $a Electronic books. $2 local $3 554714
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690 $a 0984
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a University of Maryland, College Park. $b Electrical Engineering. $3 845418
773 0 $t Dissertation Abstracts International $g 79-11B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10688744 $z click for full text (PQDT)