Why this work is in the frame
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Bibliographic record
Abstract
This thesis introduces a maximum entropy approach to model surface reflectance spectra. A reflectance spectrum is the amount of light, relative to the incident light, reflected from a surface at each wavelength. While the color of a surface can be in 3D vector form such as RGB, CMY, or YIQ, this thesis takes the surface reflectance spectrum to be the color of a surface. A reflectance spectrum is a physical property of a surface and does not vary with the different interactions a surface may undergo with its environment. Therefore, models of reflectance spectra can be used to fuse camera sensor responses from different images of the same surface or multiple surfaces of the same scene. This fusion improves the spectral estimates that can be obtained, and thus leads to better estimates of surface colors. The motivation for using a maximum entropy approach stems from the fact that surfaces observed in our everyday life surroundings typically have broad and therefore high entropy spectra. The maximum entropy approach, in addition, imposes the fewest constraints as it estimates surface reflectance spectra given only camera sensor responses. This is a major advantage over the widely used linear basis function spectral representations, which require a prespecified set of basis functions. Experimental results show that surface spectra of Munsell and construction paper patches can be successfully estimated using the maximum entropy approach in the case of three different surface interactions with the environment. First, in the case of changes in illumination, the thesis shows that the spectral models estimated are comparable to those obtained from the best approach which computes spectral models in the literature. Second, in the case of changes in the positions of surfaces with respect to each other, interreflections between the surfaces arise. Results show that the fusion of sensor responses from interreflection
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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