Object to Object Color Matchings By Image Clustering
Abstract:Object to object color matching strategy depending on the image contents is proposed. Pictorial color image is classified into different object areas with clustered color distributions. Euclidian or Mahalanobis color distance measures, and maximum likelihood method based on Bayesian decision rule, are introduced to the classification. After the objects' classification, each clustered pixels are projected onto principal component space by Hotelling transform and the color mappings are performed for the principal components to be matched in between the individual objects of original and printed images.
Document Type: Research Article
Publication date: 1998-01-01
For more than 30 years, IS&T's series of digital printing conferences have been the leading forum for discussion of advances and new directions in 2D and 3D printing technologies. A comprehensive, industry-wide conference that brings together industry and academia, this meeting includes all aspects of the hardware, materials, software, images, and applications associated with digital printing systems?particularly those involved with additive manufacturing and fabrication?including bio-printing, printed electronics, page-wide, drop-on-demand, desktop and continuous ink jet, toner-based systems, and production digital printing, as well as the engineering capability, optimization, and science involved in these fields. In 2016, the conference changed its name formally to Printing for Fabrication to better reflect the content of the meeting and the evolving technology of printing.
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