A model-based road sign recognition system /
Bibliographic record
Abstract
A road sign recognition system poses a real challenge for machine vision. It must recognize a wide variety of road signs under considerable variations in illumination and imaging geometry---all in real-time. This thesis presents a modular road sign recognition system relying on modelling for both detection and recognition. It divides into three main stages of processing. The first, concerned with detection, exploits the specific colors of road signs. The color constancy problem caused by the daylight illumination variations is addressed directly with a physics-based model supplemented by a calibration stage using real data. The second stage of processing, devoted to recognizing road signs in regions of interest found in the detection phase, involves a database containing more than 400 road signs arranged in a tree structure, and uses a novel correlation-based template matching technique relying on a bitwise encoding that accounts for both color labels and affine variations in the image formation process, and which also allows to build templates that are able to represent classes of objects. The content of the database used by the recognition algorithm is generated in a deterministic and automated manner by way of geometrical modelling of the image formation process starting with only model images of the road signs to be recognized. The recognition algorithm exploits color as a first logical classification step to direct the search for a road sign in the database, with the later finer steps being driven by correlation scores obtained from template matching. At the third stage of processing, a scene understanding module exploits constraints on the position of road signs along with the spatial relationships they must have in certain cases to other road signs in the image to filter out false positives. During processing, the system incorporates top-down mechanisms that use data fed back by partial recognitions, which allow to progressively gain more information about
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.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".