Using ga to optimize the explicitly defined skin regions for human skincolor detection
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Fast and accurate detection of human skin color is an important task in computer vision and image processing applications. Skin color detection algorithms are vital in medical application, especially in diagnosing skin diseases. This paper presents an approach for defining an explicit skin model by determining the optimal skin color regions in the selected color space. During the optimization, the skin color is defined as the union of multiple smaller regions; this is in contrast to the single region approach used in the state-of-the-art research. In this work, genetic algorithms are used to determine the boundaries of a number of skin color regions in CbCr color space to minimize the false detection rates. Using these optimized multiple regions on a challenging test dataset with uncontrolled conditions; an improvement of over 50% in the false detection is achieved compared with current CbCr based skin color detection (explicitly defined skin regions). Moreover, the proposed optimized skin model shows detection rates that are as good as Multi-layer-perceptron (MLP) and bayes classifiers with a computational cost reduction up to 80%.
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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 | 0.001 | 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