Non-uniform illumination correction in infrared images based on a modified fuzzy c-means algorithm
Why this work is in the frame
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Bibliographic record
Abstract
The correction of non-uniform illumination and the elimination of shading artifacts is an important preprocessing task used in a great number of image processing applications such as segmentation, registration or quantitative analysis. Although, a careful and accurate set up of the image acquisition system may degrade the importance of a brightness normalization algorithm, non-uniform illumination appears due to the interaction of objects and light on the scene requires retrospective shading correction. The image formation process and the corresponding shading effects are described by a linear image formation model, which consists of a multiplicative and an additive shading component. In this paper a novel brightness normalization method is proposed to eliminate the non-uniform illumination effects. The method is based on the application of a fuzzy c-means algorithm (FCM) only on the background part of the acquired image, where the objective function is modified to take into account local information of each pixel in the estimation of the multiplicative and the additive shading components. The modified FCM algorithm is iterative, as the standard FCM, and at each iteration the multiplicative and the additive shading components are re-estimated based on the cluster centers and the membership of each pixel in a specific cluster. Brightness correction is performed by the inverse of the image formation model after FCM convergence. Experiments were conducted in a database of both real and artificial infrared images. The experimental results show that the proposed method decreases significantly the non-uniform illumination effects and does not introduce brightness variations if the background is uniform.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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