Improving image segmentation via shape PCA reconstruction
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Bibliographic record
Abstract
This paper proposes a post-processing method for image segmentation to take advantage of information not directly available from the image. Specifically, the proposed method improves the segmentation of an image by making use of shape information learned from training shapes in ground truth images. To obtain shape prior, training shapes are first aligned by congealing, and then landmark interpolation is performed, followed by shape PCA on aligned shapes. To improve a segmentation, subsequently, shape PCA reconstruction is performed using the first few principal components on objects in the segmented image. Shape PCA is performed locally instead of globally, on parts of the object deemed inaccurate, using a method based on radius-vector function. Experimental results show that shape PCA reconstruction, especially local shape PCA reconstruction, improves the segmentation in an ore-size measurement application significantly.
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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 | 0.001 |
| 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