A novel hybrid approach for the extraction of linear/cylindrical features from laser scanning data
Bibliographic record
Abstract
Abstract. However, the collected point cloud should undergo manipulation approaches to be utilized for diverse civil, industrial, and military applications. Different processing techniques have consequently been implemented for the extraction of low-level features from this data. Linear/cylindrical features are among the most important primitives that could be extracted from laser scanning data, especially those collected in industrial sites and urban areas. This paper presents a novel approach for the identification, parameterization, and segmentation of these features in a laser point cloud. In the first step of the proposed approach, the points which belong to linear/cylindrical features are detected and their appropriate representation models are chosen based on the principal component analysis of their local neighborhood. The approximate direction and position parameters of the identified linear/cylindrical features are then refined using an iterative line/cylinder fitting procedure. A parameter-domain segmentation method is finally applied to isolate the points which belong to individual linear/cylindrical features in direction and position attribute spaces, respectively. Experimental results from real datasets will demonstrate the feasibility of the proposed approach for the extraction of linear/cylindrical features from laser scanning data.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".