Intensity-invariant color image segmentation using MPC algorithm
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, two unsupervised color image segmentation methods based on color clustering are explored: k-means (KM) and mixture of principal components (MPC). KM and MPC use respectively the Euclidean distance and the vector angle as color similarly measures. It is shown that the vector angle is an intensity-invariant measure in RGB based on the dichromatic reflectance model. Results are given for various color spaces: RGB, XYZ, rgb (normalized RGB), CIELAB, CIELUV, h/sub 1/h/sub 2/h/sub 3/ (a new space), and l/sub 1/l/sub 2/l/sub 3/. Quantitative and qualitative results show the effectiveness of the MPC algorithm on the RGB, rgb, and XYZ color spaces whereas the KM combination seems most effective in the CIELAB, h/sub 1/h/sub 2/h/sub 3/, and l/sub 1/l/sub 2/l/sub 3/ color spaces. Finally, poor color clustering results with MPC in h/sub 1/h/sub 2/h/sub 3/ and with KM in rgb suggest that some assumptions in deriving a simplified version of Shafer's model for matte surfaces might have been violated.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it