Video Object Segmentation and Tracking Using Probabilistic Fuzzy C-Means
Bibliographic record
Abstract
Automatic video object segmentation and tracking is a challenging problem. In this paper, we introduce a new systematic method for fully automatic object segmentation and tracking using probabilistic fuzzy c-means and Gibbs random fields. The spatial segmentation is based on probabilistic fuzzy c-means clustering and Gibbs sampling. The obtained segmented mask is then refined by taking into account of motion information. Motion vectors are calculated using block matching method based on phase correlation. The motion features and their spatial relationships are used to associate the segmented regions to form video objects. Temporal tracking is achieved by projecting the blocks in current frame to the next frame. The motion-compensated prediction is carried out directly over membership matrix which is used as the initialization of probabilistic fuzzy c-means clustering for the next frame. Experimental results show that the proposed method can automatically extract and track the video object in cluttered background. The major advantages of the proposed method are its ability to deal with deformable objects and being fully automatic
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 | 0.000 |
| 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 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".