Computing Entropy Maps of Finite-Automaton-Encoded Binary Images (Extended Abstract)
Bibliographic record
Abstract
) Mark G. Eramian Department of Computer Science University of Western Ontario London, Ontario, N6A 5B7, Canada meramian@csd.uwo.ca Abstract. Finite automata are being used to encode images. Applications of this technique include image compression, and extraction of self similarity information and Hausdorff dimension of the encoded image. Jurgensen and Staiger [6] proposed a method by which the local Hausdorff dimension of the encoded image could be effectively computed. This paper describes the first implementation of this procedure and presents some experimental results showing local entropy maps computed from images represented by finite automata. 1 Introduction Local entropy (Hausdorff dimension) measures of images are of interest because the local entropy of an image is closely related to local texture. If we can map texture, it is possible to map the boundaries of objects in the image, or to simply map relative differences over the image. Image texture mapping tech...
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 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".