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Record W2123786420 · doi:10.1111/0031-868x.t01-1-00010

Autonomous space resection using Point‐ and Line‐Based representation of FREE‐FORM control Linear Features

2003· article· en· W2123786420 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Photogrammetric Record · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicRemote Sensing and LiDAR Applications
Canadian institutions3v Geomatics (Canada)University of Calgary
Fundersnot available
KeywordsComputer visionComputer scienceArtificial intelligenceRepresentation (politics)Feature (linguistics)PhotogrammetryObject (grammar)Feature vectorPattern recognition (psychology)

Abstract

fetched live from OpenAlex

Abstract Automatic single photo resection (SPR) remains one of the challenging problems in digital photogrammetry. Visibility and uniqueness of distinct control points in the input imagery limit robust automation of the space resection procedure. Recent advances in photogrammetry mandate adopting higher‐level primitives, such as free‐form control linear features, for replacing traditional control points. Linear features can be automatically extracted from the image space. On the other hand, object space control linear features can be obtained from an existing GIS layer containing 3D vector data such as road networks or from newly developed terrestrial mobile mapping systems (MMS). In this paper, two different approaches are presented for simultaneously determining the position and attitude of the imagery as well as the correspondence between image and object space linear features. These approaches are based on two representation schemes of the linear features. The first one represents the linear feature by a sequence of 2D and 3D points along the linear feature in the image and object space, respectively. The second scheme assumes that the feature is modelled by polylines (a sequence of straight‐line segments). Neither approach requires one‐to‐one correspondence between image and object space primitives, which makes the suggested methodology robust against changes and/or discrepancies between the data‐sets involved. This characteristic will be helpful in detecting and dealing with changes between object and image space linear features (due to temporal effects for example). The parameter estimation and matching follow an optimal sequential procedure that is developed and described within this paper, which depends on the sensitivity of the mathematical model relating corresponding primitives at various image regions to incremental changes in the exterior orientation parameters (EOP). Experiments are conducted to compare the algorithms’ efficiency and the accuracy of the estimated EOP using both approaches. Experimental results using real data demonstrate the feasibility and robustness of both representation schemes as well as the methodologies developed. Moreover, different generalisation levels of the polylines representing the free‐form linear features are compared.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.553
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.021
GPT teacher head0.267
Teacher spread0.246 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it