Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-642-14400-4_47
DC FieldValue
dc.titleLearning from humanoid cartoon designs
dc.contributor.authorIslam, M.T.
dc.contributor.authorNahiduzzaman, K.M.
dc.contributor.authorPeng, W.Y.
dc.contributor.authorAshraf, G.
dc.date.accessioned2013-07-04T07:56:54Z
dc.date.available2013-07-04T07:56:54Z
dc.date.issued2010
dc.identifier.citationIslam, M.T.,Nahiduzzaman, K.M.,Peng, W.Y.,Ashraf, G. (2010). Learning from humanoid cartoon designs. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 6171 LNAI : 606-616. ScholarBank@NUS Repository. <a href="https://doi.org/10.1007/978-3-642-14400-4_47" target="_blank">https://doi.org/10.1007/978-3-642-14400-4_47</a>
dc.identifier.isbn3642143997
dc.identifier.issn03029743
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/40111
dc.description.abstractCharacter design is a key ingredient to the success of any comic-book, graphic novel, or animated feature. Artists typically use shape, size and proportion as the first design layer to express role, physicality and personality traits. In this paper, we propose a knowledge mining framework that extracts primitive shape features from finished art, and trains models with labeled metadata attributes. The applications are in shape-based query of character databases as well as label-based generation of basic shape scaffolds, providing an informed starting point for sketching new characters. It paves the way for more intelligent shape indexing of arbitrary well-structured objects in image libraries. Furthermore, it provides an excellent tool for novices and junior artists to learn from the experts. We first describe a novel primitive based shape signature for annotating character body-parts. We then use support vector machine to classify these characters using their body part's shape signature as features. The proposed data transformation is computationally light and yields compact storage. We compare the learning performance of our shape representation with a low-level point feature representation, with substantial improvement. © 2010 Springer-Verlag Berlin Heidelberg.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1007/978-3-642-14400-4_47
dc.sourceScopus
dc.subjectHumanoid Cartoons
dc.subjectPerception Modeling
dc.subjectShape Signature
dc.typeConference Paper
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.doi10.1007/978-3-642-14400-4_47
dc.description.sourcetitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.description.volume6171 LNAI
dc.description.page606-616
dc.identifier.isiutNOT_IN_WOS
Appears in Collections:Staff Publications

Show simple item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

5
checked on Aug 14, 2022

Page view(s)

159
checked on Aug 18, 2022

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.