Model-based halftoning for color image segmentation
Research output: Contribution to journal › Conference article › Research › peer-review
Grouping algorithms based on histograms over measured image features have very successfully been applied to textured image segmentation [2, 11, 6]. However; the competing goals of statistical estimation significance demanding few quantization levels versus the necessary richness in representation often prevent a successful application for the color cue, since quantization may result in contouring.
In this paper, we combine a novel halftoning technique called spatial quantization with distribution-based grouping algorithms to synthesize a powerful color image segmentation technique. The spatial quantization simultaneously determines color palette and halftoning by optimizing a joint cost function. It therefore allows for a highly adapted image representation with a smooth transition of color distributions for non-constant image surfaces.
Original language | English |
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Journal | International Conference on Pattern Recognition |
Pages (from-to) | 629-632 |
Number of pages | 4 |
ISSN | 1051-4651 |
Publication status | Published - 2000 |
Externally published | Yes |
Event | 15th International Conference on Pattern Recognition (ICPR-2000) - BARCELONA, Spain Duration: 3 Sep 2000 → 7 Sep 2000 |
Conference
Conference | 15th International Conference on Pattern Recognition (ICPR-2000) |
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Country | Spain |
City | BARCELONA |
Period | 03/09/2000 → 07/09/2000 |
ID: 302162220