Feature extraction and calibration for stereo reconstruction using non-SVP optics in a panoramic stereo-vision sensor
Bibliographic record
Abstract
Omni-directional sensors are useful in obtaining a 360/spl deg/ field of view with a single lens camera. Omni-directional stereo imaging systems were designed in our past research using a single camera and a mirror consisting of two concentric, radially symmetric lobes. If the central camera-mirror axis is vertical, a stereo image containing imagery from all azimuth directions from, two viewpoints can be captured within one image. A vertically posed catadioptric optical system that employs a mirror with a radial profile other than parabolic or hyperbolic cannot maintain the straightness of any nonvertical lines. However other mirror profiles are desirable for resolution distribution, sensor size and manufacturability. These other mirror shapes, including spherical mirrors, are said to be non-SVP (Single View-Point). Since they lack a virtual perspective point, they pose new feature extraction challenges. A method for processing the imagery from a panoramic non-SVP catadioptric stereo sensor to reconstruct a three-dimensional model of horizontal and vertical line features is introduced. Horizontal line segments are extracted using the panoramic Hough transform and vertical line segments are recognized as straight radial lines. These segments, and closed shapes they form are matched between the two lobe views to locate them in three-dimensions. The practical accuracy obtainable with such a system was explored, and some of the issues addressed to make this sensor work in real 3D reconstructions are described. Triangulating a 3D location requires better calibration information than is required for the robust feature extraction.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".