Paper
24 January 2012 Bio-inspired color sketch for eco-friendly printing
Author Affiliations +
Proceedings Volume 8292, Color Imaging XVII: Displaying, Processing, Hardcopy, and Applications; 829218 (2012) https://doi.org/10.1117/12.906736
Event: IS&T/SPIE Electronic Imaging, 2012, Burlingame, California, United States
Abstract
Saving of toner/ink consumption is an important task in modern printing devices. It has a positive ecological and social impact. We propose technique for converting print-job pictures to a recognizable and pleasant color sketches. Drawing a "pencil sketch" from a photo relates to a special area in image processing and computer graphics - non-photorealistic rendering. We describe a new approach for automatic sketch generation which allows to create well-recognizable sketches and to preserve partly colors of the initial picture. Our sketches contain significantly less color dots then initial images and this helps to save toner/ink. Our bio-inspired approach is based on sophisticated edge detection technique for a mask creation and multiplication of source image with increased contrast by this mask. To construct the mask we use DoG edge detection, which is a result of blending of initial image with its blurred copy through the alpha-channel, which is created from Saliency Map according to Pre-attentive Human Vision model. Measurement of percentage of saved toner and user study proves effectiveness of proposed technique for toner saving in eco-friendly printing mode.
© (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Ilia V. Safonov, Ekaterina V. Tolstaya, Michael N. Rychagov, Hokeun Lee, Sang Ho Kim, and Donchul Choi "Bio-inspired color sketch for eco-friendly printing", Proc. SPIE 8292, Color Imaging XVII: Displaying, Processing, Hardcopy, and Applications, 829218 (24 January 2012); https://doi.org/10.1117/12.906736
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CITATIONS
Cited by 3 scholarly publications.
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KEYWORDS
Printing

Biomimetics

Edge detection

Image enhancement

Visual process modeling

Detection and tracking algorithms

Human vision and color perception

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