07 - Analyse de scènes dynamiques complexes par la méthode du plongement fractal
Bibliographic record
Abstract
We present a new image sequence analysis method for automatic and real-time extraction of transitory and complex motions in natural scenes. We show how to extract these motions as multidimensional point clusters obtained from the temporal embedding of grey level variations, in five successive steps: embedding, fractal indexing, point chaining, cluster identification and data extraction. We develop the two main algorithms: fractal space filling indexing and chaining in order to access directly to the relevant information. To illustrate our method, we present an automatic system for early smoke source detection through the processing of landscape images by extracting fugitive and various movements within a small spot of pixels affected by the smoke. We show how to modify the embedding technique used to obtain the data points coordinates to produce many other applications for the fractal embedding method, for example the recognition of complex moving or varying shapes objects.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".