Locale-based object search under illumination change using chromaticity voting and elastic correlation
Bibliographic record
Abstract
Searching for an object model is considered to be one of the most desirable and yet difficult searches. The problem is made difficult by the presence of clutter in a scene, as well as the fact that objects may be imaged under different lighting conditions. We have developed a feature localization scheme that finds a set of locales in an image. Our object search method matches image locales with model object locales. We make use of a diagonal model for illumination change so that each candidate assignment of model to image locales produces a possible set of lighting transformation coefficients in chromaticity space. A combinatoric search for the locale assignment problem is obviated by matching each model locale to every image locale and carrying out a voting scheme in the space of lighting coefficients. This efficiently finds the lighting change. As well, for each pair of coefficients we perform an elastic correlation on locale chromaticity. Locale centroids produce a pose estimation via a displacement model, and we can further apply texture histogram intersection and finally a generalized Hough transform efficiently since the rotation, scale and translation parameters have been recovered. Tests on a database of real images and videos show good image retrieval results.
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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 | 0.001 |
| 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".