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Machine Learning for Deep Image Manipulation.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine Learning for Deep Image Manipulation./
Author:	Park, Taesung.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	154 p.
Notes:	Source: Dissertations Abstracts International, Volume: 83-03, Section: B.
Contained By:	Dissertations Abstracts International83-03B.
Subject:	Computer science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28540031
ISBN:	9798535552675

Machine Learning for Deep Image Manipulation.
Park, Taesung.

Machine Learning for Deep Image Manipulation. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 154 p.

Source: Dissertations Abstracts International, Volume: 83-03, Section: B.

Thesis (D.Eng.)--University of California, Berkeley, 2021.

This item must not be sold to any third party vendors.

Common types of image editing methods focus on low-level characteristics. In this thesis, I leverage machine learning to enable image editing that operates at a higher conceptual level. Fundamentally, the proposed methods aim to factor out the visual information that must be maintained in the editing process from the information that may be edited by incorporating the generic visual knowledge. As a result, the new methods can transform images in human-interpretable ways, such as turning one object into another, stylizing photographs into a specific artist's paintings, or adding sunset to a photo taken in daylight. We explore designing such methods in different settings with varying amounts of supervision: per-pixel labels, per-image labels, and no labels. First, using per-pixel supervision, I propose a new deep neural network architecture that can synthesize realistic images from scene layouts and optional target styles. Second, using per-image supervision, I explore the task of domain translation, where an input image of one class is transformed into another. Lastly, I design a framework that can still discover disentangled manipulation of structure and texture from a collection of unlabeled images. We present convincing visuals in a wide range of applications including interactive photo drawing tools, object transfiguration, domain gap reduction between virtual and real environment, and realistic manipulation of image textures.

ISBN: 9798535552675Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Computational photography

Machine Learning for Deep Image Manipulation.
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245 1 0 $a Machine Learning for Deep Image Manipulation.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 154 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-03, Section: B.
500 $a Advisor: Efros, Alexei A.
502 $a Thesis (D.Eng.)--University of California, Berkeley, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a Common types of image editing methods focus on low-level characteristics. In this thesis, I leverage machine learning to enable image editing that operates at a higher conceptual level. Fundamentally, the proposed methods aim to factor out the visual information that must be maintained in the editing process from the information that may be edited by incorporating the generic visual knowledge. As a result, the new methods can transform images in human-interpretable ways, such as turning one object into another, stylizing photographs into a specific artist's paintings, or adding sunset to a photo taken in daylight. We explore designing such methods in different settings with varying amounts of supervision: per-pixel labels, per-image labels, and no labels. First, using per-pixel supervision, I propose a new deep neural network architecture that can synthesize realistic images from scene layouts and optional target styles. Second, using per-image supervision, I explore the task of domain translation, where an input image of one class is transformed into another. Lastly, I design a framework that can still discover disentangled manipulation of structure and texture from a collection of unlabeled images. We present convincing visuals in a wide range of applications including interactive photo drawing tools, object transfiguration, domain gap reduction between virtual and real environment, and realistic manipulation of image textures.
590 $a School code: 0028.
650 4 $a Computer science. $3 523869
650 4 $a Research. $3 531893
650 4 $a Internships. $3 3560137
650 4 $a Photographs. $3 627415
650 4 $a Datasets. $3 3541416
650 4 $a Experiments. $3 525909
650 4 $a Adaptation. $3 3562958
650 4 $a Methods. $3 3560391
650 4 $a Advisors. $3 3560734
650 4 $a Ablation. $3 3562462
650 4 $a Semantics. $3 520060
650 4 $a Editing. $3 601456
650 4 $a Painting. $3 524049
650 4 $a Artificial intelligence. $3 516317
653 $a Computational photography
653 $a Computer vision
653 $a Deep learning
653 $a Image editing
653 $a Machine learning
690 $a 0984
690 $a 0800
710 2 $a University of California, Berkeley. $b Electrical Engineering & Computer Sciences. $3 1671057
773 0 $t Dissertations Abstracts International $g 83-03B.
790 $a 0028
791 $a D.Eng.
792 $a 2021
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28540031