Region Tracking Via Local Statistics And Level Set Pdes
Bibliographic record
Abstract
this paper, we propose a novel algorithm for region tracking that uses the Bayesian framework for tracking previously developed [1]. We extend this framework by re-expressing tracking in terms of Kullback-Leibler divergence of specific probability distributions and generalizing these to empirical distributions computed over image neighborhoods, leading to level set equations in terms of local image statistics. The main novelty of our proposed algorithm is that contrary to other tracking algorithms which are expressed as level set PDEs, the motion is not assumed to be small [2], nor is the background assumed to be stationary [3], nor is the region supposed to be uniform and have strong contrast with the background [4]. We illustrate the performance of our algorithm on real image sequences with natural motion. 2. REGION TRACKING VIA LOCAL STATISTICS 2.1. Basic Models and Level Set Evolution Equations \t be images at time instants . Let the domain of both images be . Let be a region in the image at time ( ) and let be the corresponding (unknown) region in the image at time ), that we seek to estimate. Assume there exists a given (finite or infinite) set of diffeomorphisms ! !"# , and that there exists a mapping $&%' with $()*+-,. (and hence $/()10 +/,210 ) such that $()5/+6, ()56+786()5/+ :9 5;%< denotes a stationary zero-mean Gaussian white noise process with variance => . Most tracking algorithms estimate estimating ; from the estimate ? $ , the estimate then computed as ,A?$/() + . As a result, strong assumptions This work was supported by the Natural Sciences and Engineering Research Council of Canada under Strategic Grant STR224122
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".