NDLI: Learning how to match fresco fragments

Please wait, while we are loading the content...

Learning how to match fresco fragments

Content Provider	ACM Digital Library
Author	Funkhouser, Thomas Shin, Hijung Toler-Franklin, Corey Castaeda, Antonio Garca Brown, Benedict Dobkin, David Rusinkiewicz, Szymon Weyrich, Tim
Copyright Year	2011
Abstract	One of the main problems faced during reconstruction of fractured archaeological artifacts is sorting through a large number of candidate matches between fragments to find the relatively few that are correct. Previous computer methods for this task provided scoring functions based on a variety of properties of potential matches, including color and geometric compatibility across fracture surfaces. However, they usually consider only one or at most a few properties at once, and therefore provide match predictions with very low precision. In this article, we investigate a machine learning approach that computes the probability that a match is correct based on the combination of many features. We explore this machine learning approach for ranking matches in three different sets of fresco fragments, finding that classifiers based on many match properties can be significantly more effective at ranking proposed matches than scores based on any single property alone. Our results suggest that it is possible to train a classifier on match properties in one dataset and then use it to rank predicted matches in another dataset effectively. We believe that this approach could be helpful in a variety of cultural heritage reconstruction systems.
Starting Page	1
Ending Page	13
Page Count	13
File Format	PDF
ISSN	15564673
e-ISSN	15564711
DOI	10.1145/2037820.2037824
Journal	Journal on Computing and Cultural Heritage (JOCCH)
Volume Number	4
Issue Number	2
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2011-11-01
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Shape matching Cultural heritage computer-assisted fresco reconstruction Machine learning
Content Type	Text
Resource Type	Article
Subject	Computer Graphics and Computer-Aided Design Conservation Computer Science Applications Information Systems

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Semantic fitting and reconstruction

Factors Facilitating Cultural Learning in Virtual Architectural Heritage Environments: End User Perspective

Multi-feature matching of fresco fragments

Design of cultural heritage information systems based on information layers

A Discrete Approach for Pairwise Matching of Archaeological Fragments

Journal on Computing and Cultural Heritage (JOCCH)

Introduction to special issue on serious games for cultural heritage

A taxonomy of visualization strategies for cultural heritage applications

Research challenges for digital archives of 3D cultural heritage models

Learning how to match fresco fragments

Similar Documents

Semantic fitting and reconstruction

Factors Facilitating Cultural Learning in Virtual Architectural Heritage Environments: End User Perspective

Multi-feature matching of fresco fragments

Design of cultural heritage information systems based on information layers

A Discrete Approach for Pairwise Matching of Archaeological Fragments

Journal on Computing and Cultural Heritage (JOCCH)

Introduction to special issue on serious games for cultural heritage

A taxonomy of visualization strategies for cultural heritage applications

Research challenges for digital archives of 3D cultural heritage models

Learning how to match fresco fragments