Paper Info
Title
Semi-Automatic Object Geometry Estimation For Image Personalization
Abstract
Digital printing brings about a host of benefits, one of which is the ability to create short runs of variable, customized content. One form of customization that is receiving much attention lately is in photofinishing applications, whereby personalized calendars, greeting cards, and photo books are created by inserting text strings into images. It is particularly interesting to estimate the underlying geometry of the surface and incorporate the text into the image content in an intelligent and natural way. Current solutions either allow fixed text insertion schemes into preprocessed images, or provide manual text insertion tools that are time consuming and aimed only at the high-end graphic designer. It would thus be desirable to provide some level of automation in the image personalization process.We propose a semi-automatic image personalization workflow which includes two scenarios: text insertion and text replacement. In both scenarios, the underlying surfaces are assumed to be planar. A 3-D pinhole camera model is used for rendering text, whose parameters are estimated by analyzing existing structures in the image. Techniques in image processing and computer vison such as the Hough transform, the bilateral filter, and connected component analysis are combined, along with necessary user inputs. In particular, the semi- automatic workflow is implemented as an image personalization tool, which is presented in our companion paper. 1 Experimental results including personalized images for both scenarios are shown, which demonstrate the effectiveness of our algorithms.
Year
DOI
Venue
2010
10.1117/12.843828
COMPUTATIONAL IMAGING VIII
Keywords
DocType
Volume
image personalization, text insertion, text replacement, planar surfaces, 3-D pinhole camera model, the Hough transform
Conference
7533
ISSN
Citations 
PageRank 
0277-786X
1
0.45
References 
Authors
1
6
Name
Order
Citations
PageRank
Hengzhou Ding151.37
Raja Bala29917.72
Zhigang Fan358265.45
Reiner Eschbach418438.95
Charles A. Bouman52740473.62
Jan P. Allebach61230170.88