A Study on Lip Extraction due to Fuzzy Reasoning by Using Color Information
Bibliographic record
Abstract
This paper proposes a method for extracting the lip shape from the region around the lip. It is carried out without limited conditions such as the lipstick or lighting. The proposed method uses color information for the lip shape extraction. They are psychometric quantities of a metric hue angle (hab), a rectangular coordinates(a*) , which are defined in CIE 1976 L*a*b* color space. The method employs fuzzy reasoning was employed in order to consider obscurity in image data such as shade on the face. The membership function of condition part for characteristics in each class was defined by the triangular membership function was used for the fuzzy reasoning. In order to reduce the effect of the data acquisition condition, an extraction method is here presented for the lip shape from region around the lip due to fuzzy reasoning. We studied on set up of the membership function of condition part. The proposed method uses a* color histogram and habcolor histogram of the region around the lip when sets up of membership function of condition part.This paper clarified that the lip was able to be extracted without the no special conditions such as the lipstick or lighting. The experimental result indicates the effectiveness of the propose method; about 98.7 percent of facial images data was extracted its shape.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.006 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".