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Record W2142446297 · doi:10.1109/iita.2009.310

An Optimalizing Threshold Segmentation Algorithm for Road Images Based on Mathematical Morphology

2009· article· en· W2142446297 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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Measurement and Detection Methods
Canadian institutionsnot available
FundersGuangdong Ocean UniversityLakehead University
KeywordsMathematical morphologySegmentationRobustness (evolution)Image segmentationComputer scienceClosing (real estate)Artificial intelligenceShadow (psychology)Computer visionAlgorithmScale-space segmentationMobile robotNoise (video)Morphological gradientRobotPattern recognition (psychology)Image (mathematics)Image processing

Abstract

fetched live from OpenAlex

Considering the requirements of real time and robustness for the segmentation algorithms for mobile robots, an optimal threshold segmentation algorithm based on mathematical morphology was studied. Two improved measures, i.e., introducing an improved combination filter of median and multi-scale, and increasing morphological opening and closing operation, were proposed for the general optimal threshold segmentation algorithm. The experimental results indicated that the algorithm can not only accurately segment road and off-road regions within the unstructured road images that contain three common types of noise, but better overcome the influence of the road shadow, water marks, cracks and damage on the segmentation.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.539
Threshold uncertainty score0.401

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.033
GPT teacher head0.329
Teacher spread0.295 · 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