A comparison of PCA and ICA for object recognition under varying illumination
Why this work is in the frame
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Bibliographic record
Abstract
An experiment is performed to evaluate the ability of two different subspace methods to recognize objects under different illumination conditions. The principal component analysis (PCA) and independent component analysis (ICA) are compared for classifying 25 different objects with varying degrees of specularity under different illumination. Each object was sampled under three widely different lighting conditions to form a set of training images used to create subspaces with dimensions ranging from 10 to 30 basis vectors. The efficacy of ICA and PCA to correctly classify the objects was tested using two test images for each object under unique lighting conditions not included in the training set. The results were also determined when the images were pre-filtered with a Laplacian of Gaussian filter. Results show that ICA techniques show promise for object recognition under varying illumination conditions.
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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 | 0.000 | 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 it