Depth and Content Perception Enhance Word Art

• Main Authors: Sheng-yuanChen, 陳聲遠
• Other Authors: Tong-Yee Lee
• Format: Others
• Language: en_US
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/90961388213327941878

碩士 === 國立成功大學 === 資訊工程學系 === 104 === Text visualization intuitively express a summary composited with graphic symbol, and word art composite with the size and position of font character to generate a stylized result. Previous work art in computer graphics focus on the 2D word art representation in a simple shape and boundary. We introduce a word art with detailed context by considering the depth of the input image. Given a 2.5D model by analyzing the depth information of input image. First, we use mesh parameterization to build a depth enhance texture coordinate. Second, we build an interactive system to assist user to design the word block. An eye tracking visual model is used to explore the attention distribution over an image to guide user to place the key word on most salient image. To render the word with depth enhance, we generate the word cloud in partial regions on the mesh parameterization. We demonstrate a word art with depth and context enhancement compared to several artist works.