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Image Quality and Beauty Classification Using Deep Learning.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Image Quality and Beauty Classification Using Deep Learning.
作者:	Golchubian, Arash.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, 2022
面頁冊數:	97 p.
附註:	Source: Dissertations Abstracts International, Volume: 84-03, Section: A.
附註:	Advisor: Nojoumian, Mehrdad.
Contained By:	Dissertations Abstracts International84-03A.
標題:	Computer science.
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29327298
ISBN:	9798841796688

Image Quality and Beauty Classification Using Deep Learning.
Golchubian, Arash.

Image Quality and Beauty Classification Using Deep Learning. - Ann Arbor : ProQuest Dissertations & Theses, 2022 - 97 p.

Source: Dissertations Abstracts International, Volume: 84-03, Section: A.

Thesis (Ph.D.)--Florida Atlantic University, 2022.

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

The field of computer vision has grown by leaps and bounds in the past decade. The rapid advances can be largely attributed to advances made in the field of Artificial Neural Networks and more specifically can be attributed to the rapid advancement of Convolutional Neural Networks (CNN) and Deep Learning. One area that is of great interest to the research community at large is the ability to detect the quality of images in the sense of technical parameters such as blurriness, encoding artifacts, saturation, and lighting, as well as for its’ aesthetic appeal. The purpose of such a mechanism could be detecting and discarding noisy, blurry, dark, or over exposed images, as well as detecting images that would be considered beautiful by a majority of viewers. In this dissertation, the detection of various quality and aesthetic aspects of an image using CNNs is explored. This research produced two datasets that are manually labeled for quality issues such as blur, poor lighting, and digital noise, and for their aesthetic qualities, and Convolutional Neural Networks were designed and trained using these datasets. Lastly, two case studies were performed to show the real-world impact of this research to traffic sign detection and medical image diagnosis.

ISBN: 9798841796688Subjects--Topical Terms:

199325
Computer science.
Subjects--Index Terms:

CNN

Image Quality and Beauty Classification Using Deep Learning.
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