Sparse Disparity Map from Uncalibrated Infrared Stereo Images
Bibliographic record
Abstract
With the rapid growth in infrared sensor technology and its drastic cost reduction, the potential of application of these imaging technologies in computer vision systems has increased. One potential application for IR imaging is depth from stereo. It has been shown that the quality of uncooled sensors is not sufficient for generating dense depth maps. In this paper we investigate the production of sparse disparity maps for uncalibrated infrared stereo images, which necessitates a robust feature-based stereo matching technique capable of dealing with the problems of infrared images, such as low resolution and high noise. Initially, a set of stable and tractable features are extracted from stereo pairs using the phase congruency model. Then, a set of Log-Gabor wavelet coefficients in different orientations and frequencies are used to analyze and describe the extracted features for matching. Finally, epipolar geometrical constraints are employed to refine the matching results. Experiments on a set of IR stereo pairs validate the robustness of our technique.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".