國立虎尾科技大學 |

From flat to hierarchical : = Modeling structures in visual recognition.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	From flat to hierarchical :/
Reminder of title:	Modeling structures in visual recognition.
Author:	Lan, Tian.
Description:	1 online resource (103 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 75-07(E), Section: B.
Subject:	Computer science. -
Online resource:	click for full text (PQDT)
ISBN:	9780499239419

From flat to hierarchical : = Modeling structures in visual recognition.
Lan, Tian.

From flat to hierarchical :Modeling structures in visual recognition. - 1 online resource (103 pages)

Source: Dissertation Abstracts International, Volume: 75-07(E), Section: B.

Thesis (Ph.D.)--Simon Fraser University (Canada), 2013.

Includes bibliographical references

Visual recognition is a fundamental problem in computer vision. It is significant to many applications such as surveillance, security, entertainment and health care. We have observed tremendous growth in visual recognition over the past decade. However, it remains a challenging problem for computers. One of the main reasons is the clear gap between human descriptions of the visual world and the output of the current visual recognition system. The semantic space humans used to describe the visual world is highly structural - besides naming an object (action), human would additionally describe it in multiple levels of detail, ranging from the fine-grained descriptions (e.g. color, shape) to the higher-level relationships among multiple objects (actions).

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

Mode of access: World Wide Web

ISBN: 9780499239419Subjects--Topical Terms:

573171
Computer science.
Index Terms--Genre/Form:

554714
Electronic books.

From flat to hierarchical : = Modeling structures in visual recognition.
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100 1 $a Lan, Tian. $3 1186804
245 1 0 $a From flat to hierarchical : $b Modeling structures in visual recognition.
264 0 $c 2013
300 $a 1 online resource (103 pages)
336 $a text $b txt $2 rdacontent
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500 $a Source: Dissertation Abstracts International, Volume: 75-07(E), Section: B.
500 $a Adviser: Greg Mori.
502 $a Thesis (Ph.D.)--Simon Fraser University (Canada), 2013.
504 $a Includes bibliographical references
520 $a Visual recognition is a fundamental problem in computer vision. It is significant to many applications such as surveillance, security, entertainment and health care. We have observed tremendous growth in visual recognition over the past decade. However, it remains a challenging problem for computers. One of the main reasons is the clear gap between human descriptions of the visual world and the output of the current visual recognition system. The semantic space humans used to describe the visual world is highly structural - besides naming an object (action), human would additionally describe it in multiple levels of detail, ranging from the fine-grained descriptions (e.g. color, shape) to the higher-level relationships among multiple objects (actions).
520 $a How to represent and learn the rich structures in the visual data is the focus of this dissertation. We address two fundamental problems in visual recognition: understanding human activities and understanding images. For solving both problems, we start with flat structures and move towards richer hierarchical structures: First, we develop figure-centric models for joint action recognition and localization that capture the spatial-temporal arrangements of an action over video sequences. Then, we propose hierarchical models for recognizing multi-person activities in entire scenes. Multiple levels of detail including actions, social roles and a scene-level event are encoded in a unified learning framework. For understanding images, we follow the same route by first developing flat models to capture the spatial structures in object queries for image retrieval, and then move towards hierarchical models to handle more complex multi-level semantic labelings for object detection.
520 $a This dissertation contributes to visual recognition by learning structured models, and in particular, hierarchical models for multi-level activity recognition and object detection. The work presented in this dissertation attempts to provide insights into several critical and yet open questions in visual recognition: How to label a visual entity (action, object, scene)? How many levels of detail should we consider? How should a recognition problem be represented? How to model the complex structures? What is the desirable output of a recognition system?
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Computer science. $3 573171
655 7 $a Electronic books. $2 local $3 554714
690 $a 0984
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Simon Fraser University (Canada). $3 1184300
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=NS23941 $z click for full text (PQDT)