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Record W2041334995 · doi:10.1364/ao.45.003022

Application of integrated sensing and processing decision trees for target detection and localization on digital mirror array imagery

2006· article· en· W2041334995 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

VenueApplied Optics · 2006
Typearticle
Languageen
FieldEngineering
TopicRemote-Sensing Image Classification
Canadian institutionsLockheed Martin (Canada)
Fundersnot available
KeywordsHyperspectral imagingComputer scienceHadamard transformComputer visionData cubeArtificial intelligenceImage processingRemote sensingDigital image processingSet (abstract data type)SuiteData setData processingPattern recognition (psychology)OpticsData miningGeologyImage (mathematics)Physics

Abstract

fetched live from OpenAlex

We demonstrate the applicability of integrated sensing and processing decision trees (ISPDTs) methodology to a set of digital mirror array (DMA) hyperspectral imagery. In particular, we demonstrate that ISPDTs can be used to detect and localize targets by using just a few DMA Hadamard frames, so that an entire hyperspectral data cube need not be collected to successfully perform the given task. This suggests that such an integrated sensing-processing suite may be appropriate for extremely time-sensitive pattern-recognition applications.

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: none
Teacher disagreement score0.762
Threshold uncertainty score0.557

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 head0.007
GPT teacher head0.206
Teacher spread0.200 · 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