東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Automatic Pigment Classification in ...

Kleynhans, Tania.

FindBook

Google Book

Amazon

博客來

Automatic Pigment Classification in Painted Works of Art from Diffuse Reflectance Image Data.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Automatic Pigment Classification in Painted Works of Art from Diffuse Reflectance Image Data./
作者:	Kleynhans, Tania.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
面頁冊數:	138 p.
附註:	Source: Dissertations Abstracts International, Volume: 82-08, Section: B.
Contained By:	Dissertations Abstracts International82-08B.
標題:	Systems science. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28264146
ISBN:	9798569978526

Automatic Pigment Classification in Painted Works of Art from Diffuse Reflectance Image Data.
Kleynhans, Tania.

Automatic Pigment Classification in Painted Works of Art from Diffuse Reflectance Image Data. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 138 p.

Source: Dissertations Abstracts International, Volume: 82-08, Section: B.

Thesis (Ph.D.)--Rochester Institute of Technology, 2020.

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

Information about artists' materials used in paintings, obtained from the analysis of limited micro-samples, has assisted conservators to better define treatment plans, and provided scholars with basic information about the working methods of the artists. Recently, macro-scale imaging systems such as visible-to-near infrared (VNIR) reflectance hyperspectral imaging (HSI) are being used to provide conservators and art historians with a more comprehensive understanding of a given work of art. However, the HSI analysis process has not been streamlined and currently requires significant manual input by experts. Additionally, HSI systems are often too expensive for small to mid-level museums. This research focused on three main objectives: 1) adapt existing algorithms developed for remote sensing applications to automatically create classification and abundance maps to significantly reduce the time to analyze a given artwork, 2) create an end-to-end pigment identification convolutional neural network to produce pigment maps that may be used directly by conservation scientists without further analysis, and 3) propose and model the expected performance of a low-cost fiber optic single point multispectral system that may be added to the scanning tables already part of many museum conservation laboratories. Algorithms developed for both classification and pigment maps were tested on HSI data collected from various illuminated manuscripts. Results demonstrate the potential of both developed processes. Band selection studies indicates that pigment identification from a small number of bands produces similar results to that of the HSI data sets on a selected number of test artifacts. A system level analysis of the proposed system was conducted with a detailed radiometric model. The system trade study confirmed the viability of using either individual spectral filters or a linear variable filter set-up to collect multispectral data for pigment identification of works of art.

ISBN: 9798569978526Subjects--Topical Terms:

3168411
Systems science.
Subjects--Index Terms:

Hyperspectral

Automatic Pigment Classification in Painted Works of Art from Diffuse Reflectance Image Data.
LDR:03400nmm a2200469 4500 001 2280096
005 20210823091518.5
008 220723s2020 ||||||||||||||||| ||eng d
020 $a 9798569978526
035 $a (MiAaPQ)AAI28264146
035 $a AAI28264146
040 $a MiAaPQ $c MiAaPQ
100 1 $a Kleynhans, Tania. $3 3558599
245 1 0 $a Automatic Pigment Classification in Painted Works of Art from Diffuse Reflectance Image Data.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2020
300 $a 138 p.
500 $a Source: Dissertations Abstracts International, Volume: 82-08, Section: B.
500 $a Advisor: Messinger, David W.
502 $a Thesis (Ph.D.)--Rochester Institute of Technology, 2020.
506 $a This item must not be sold to any third party vendors.
520 $a Information about artists' materials used in paintings, obtained from the analysis of limited micro-samples, has assisted conservators to better define treatment plans, and provided scholars with basic information about the working methods of the artists. Recently, macro-scale imaging systems such as visible-to-near infrared (VNIR) reflectance hyperspectral imaging (HSI) are being used to provide conservators and art historians with a more comprehensive understanding of a given work of art. However, the HSI analysis process has not been streamlined and currently requires significant manual input by experts. Additionally, HSI systems are often too expensive for small to mid-level museums. This research focused on three main objectives: 1) adapt existing algorithms developed for remote sensing applications to automatically create classification and abundance maps to significantly reduce the time to analyze a given artwork, 2) create an end-to-end pigment identification convolutional neural network to produce pigment maps that may be used directly by conservation scientists without further analysis, and 3) propose and model the expected performance of a low-cost fiber optic single point multispectral system that may be added to the scanning tables already part of many museum conservation laboratories. Algorithms developed for both classification and pigment maps were tested on HSI data collected from various illuminated manuscripts. Results demonstrate the potential of both developed processes. Band selection studies indicates that pigment identification from a small number of bands produces similar results to that of the HSI data sets on a selected number of test artifacts. A system level analysis of the proposed system was conducted with a detailed radiometric model. The system trade study confirmed the viability of using either individual spectral filters or a linear variable filter set-up to collect multispectral data for pigment identification of works of art.
590 $a School code: 0465.
650 4 $a Systems science. $3 3168411
650 4 $a Materials science. $3 543314
650 4 $a Remote sensing. $3 535394
650 4 $a Technical communication. $3 3172863
650 4 $a Fine arts. $3 2122690
650 4 $a Applied physics. $3 3343996
650 4 $a Arts management. $3 3168382
650 4 $a Information technology. $3 532993
653 $a Hyperspectral
653 $a Neural Network
653 $a Pigment identification
653 $a Trade study
653 $a Diffuse reflectance image data
653 $a Pigment mapping
653 $a Artist tools
653 $a Artist materials
690 $a 0794
690 $a 0790
690 $a 0643
690 $a 0357
690 $a 0489
690 $a 0424
690 $a 0215
690 $a 0799
710 2 $a Rochester Institute of Technology. $b Imaging Science. $3 1019498
773 0 $t Dissertations Abstracts International $g 82-08B.
790 $a 0465
791 $a Ph.D.
792 $a 2020
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28264146