Shape constrained discrete dynamic contours for noisy object segmentation
Bibliographic record
Abstract
In this paper, we focus on using shape information of a known class of objects to match an active contour model (ACM) with the boundary of an object in a noisy scene. The problem is addressed as finding two similar shapes in the presence of noise, where the edges of the desired object are not clearly distinguishable, during the iterative process of finding the energy-minimized active contour. The shape information is obtained through the transformation, scale, and rotation invariant Fourier descriptors (FD). During a training phase, the principal component analysis (PCA) of the FD is performed to find the modes of the variation of the FD. This is different from active shape models (ASM) that deal with shape through a point distribution model (PDM) in the spatial domain. Our proposed method overcomes the difficulties associated with landmark localization and shape normalization in ASM. We replace the shape constraint with the internal energy of ACM. The contour is constrained to an allowable space in FD domain (shape information) and cannot freely deform according to the external energy. Experimental results show a faster convergence in comparison to the original ACM and less user interaction in the training phase compared to ASM. Also, the resulting contours are more similar to the mean of the expert's manual segmentation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".