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Author Xie, Xuexiang ♦ Tian, Feng ♦ Seah, Hock Soon
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Language English
Subject Keyword Texture synthesis ♦ Perceptual similarity ♦ Artistic style transfer ♦ Nonphotorealistic rendering ♦ Stylized shading
Abstract The objective of artistic style learning is to synthesize a new image from a source image with the style learnt from example images. Existing example-based texture synthesis (EBTS) techniques model style with low-level statistical properties. These methods work well with some artistic styles such as oil painting, but have difficulties in preserving image details and features for other styles such as pencil hatching. In this article, an improved artistic style-learning algorithm with feature-based texture synthesis (FBTS) is introduced. Compared with existing EBTS methods, in our FBTS algorithm, image details and features are better defined with a feature field generated from the source image. Also, an improved L2 neighborhood distance metric which provides better measures of perceptual similarity is proposed. Results and comparisons are given to demonstrate the effectiveness of the FBTS algorithm with applications in the areas of stylized shading and artistic style transfer.
Description Affiliation: Nanyang Technological University, Singapore (Xie, Xuexiang; Tian, Feng; Seah, Hock Soon)
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2008-03-01
Publisher Place New York
Journal Computers in Entertainment (CIE) (CIE)
Volume Number 6
Issue Number 4
Page Count 13
Starting Page 1
Ending Page 13


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Source: ACM Digital Library