Semi-automatic object geometry estimation for image personalization

Hengzhou Ding; Raja Bala; Zhigang Fan; Reiner Eschbach; Charles A. Bouman; Jan P. Allebach

doi:10.1117/12.843828

5 February 2010 Semi-automatic object geometry estimation for image personalization

Hengzhou Ding, Raja Bala, Zhigang Fan, Reiner Eschbach, Charles A. Bouman, Jan P. Allebach

Proceedings Volume 7533, Computational Imaging VIII; 753304 (2010) https://doi.org/10.1117/12.843828
Event: IS&T/SPIE Electronic Imaging, 2010, San Jose, California, United States

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.

Citation Download Citation

Hengzhou Ding, Raja Bala, Zhigang Fan, Reiner Eschbach, Charles A. Bouman, and Jan P. Allebach "Semi-automatic object geometry estimation for image personalization", Proc. SPIE 7533, Computational Imaging VIII, 753304 (5 February 2010); https://doi.org/10.1117/12.843828

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