Illumination invariant representation of natural images for visual place recognition
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Illumination changes are a typical problem for many outdoor long-term applications such as visual place recognition. Keypoints may fail to match between images taken at the same location but different times of the day. Although recently some methods are presented for creating shadow-free image representations, all of them have the limitation in terms of dealing with night images and non-Planckian source of lighting. In this paper we present a new method for creating illumination invariant image representation using a combination of two existing methods based on natural image statistics that address the issue of illumination invariance. Unlike previous attempts at solving the problem of illumination invariant representation, the proposed method does not assume the ideal narrow-band color camera nor a calibration step for each environment. We evaluate our method on real datasets to establish its accuracy and efficiency. Experimental results show that our method outperforms competing methods for illumination invariant image representation.
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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 | 0.001 |
| 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 it