Deformable Object Segmentation and Contour Tracking in Image Sequences Using Unsupervised Networks
Why this work is in the frame
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Bibliographic record
Abstract
The paper discusses a novel unsupervised learning approach for tracking deformable objects manipulated by a robotic hand in a series of images collected by a video camera. The object of interest is automatically segmented from the initial frame in the sequence. The segmentation is treated as clustering based on color information and spatial features and an unsupervised network is employed to cluster each pixel of the initial frame. Each pixel from the clustering results is then classified as either object of interest or background and the contour of the object is identified based on this classification. Using static (color) and dynamic (motion between frames) information, the contour is then tracked with an algorithm based on neural gas networks in the sequence of images. Experiments performed under different conditions reveal that the method tracks accurately the test objects even for severe contour deformations, is fast and insensitive to smooth changes in lighting, contrast and background.
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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 | 0.001 | 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