New Classes of Radiometric and Combined Radiometric-Geometric Invariant Descriptors
Bibliographic record
Abstract
Real images can contain geometric distortions as well as photometric degradations. Analysis and characterization of those images without recourse to either restoration or geometric standardization is of great importance for the computer vision community as those two processes are often ill-posed problems. To this end, it is necessary to implement image descriptors that make it possible to identify the original image in a simple way independently of the imaging system and imaging conditions. Ideally, descriptors that capture image characteristics must be invariant to the whole range of geometric distortions and photometric degradations, such as blur, that may affect the image. In this paper, we introduce two new classes of radiometric and/or geometric invariant descriptors. The first class contains two types of radiometric invariant descriptors. The first of these type is based on the Mellin transform and the second one is based on central moments. Both descriptors are invariant to contrast changes and to convolution with any kernel having a symmetric form with respect to the diagonals. The second class contains two subclasses of combined invariant descriptors. The first subclass includes central-moment-based descriptors invariant simultaneously to horizontal and vertical translations, to uniform and anisotropic scaling, to stretching, to convolution, and to contrast changes. The second subclass contains central-complex-moment-based descriptors that are simultaneously invariant to similarity transformation and to contrast changes. We apply these invariant descriptors to the matching of geometric transformed and/or blurred images. Experimental results confirm both the robustness and the effectiveness of the proposed invariants.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".