Model-based halftoning for color image segmentation
Research output: Contribution to journal › Conference article › Research › peer-review
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Model-based halftoning for color image segmentation. / Puzicha, J; Belongie, S.
In: International Conference on Pattern Recognition, 2000, p. 629-632.Research output: Contribution to journal › Conference article › Research › peer-review
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TY - GEN
T1 - Model-based halftoning for color image segmentation
AU - Puzicha, J
AU - Belongie, S
PY - 2000
Y1 - 2000
N2 - 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.
AB - 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.
M3 - Conference article
SP - 629
EP - 632
JO - Proceedings - International Conference on Pattern Recognition
JF - Proceedings - International Conference on Pattern Recognition
SN - 1051-4651
T2 - 15th International Conference on Pattern Recognition (ICPR-2000)
Y2 - 3 September 2000 through 7 September 2000
ER -
ID: 302162220