Stable and Invertible Generic Fourier Descriptors for Shape-Based Image Classification
Bibliographic record
Abstract
The increasing consumer demand for smart surveillance devices capable of behavior analysis and danger alerting has led to the utilization of various descriptors for image recognition across applications, independent of image position, orientation, and scale. Despite this, few studies have focused on the completeness property of invariant descriptors for grayscale, color, and three-dimensional images. Completeness ensures descriptor distinctiveness for specific shapes, a property that is challenging to achieve and, at times, has been accessible for planar curves. The recently introduced concept of invariants’ invertibility, associated with completeness, facilitates the reconstruction of an object’s shape through similar transformations. This paper proposes a Stable and Invertible Generic Fourier Descriptor (SIGFD) for gray-level images. We demonstrate the invariance, convergence, and completeness properties of the proposed SIGFD set. To assess its robustness, we conduct experimental evaluations on well-known datasets such as Kimia 99, MPEG-7, and COIL100. Additionally, we utilize our proprietary FSTEF Faces dataset to further evaluate face recognition performance. The effectiveness of the proposed SIGFD sets is evidenced through the various studies presented in this work.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".