MétaCan
Menu
Back to cohort
Record W2040099536 · doi:10.5539/cis.v5n1p97

Rotation and Scaling Image Using PCA

2011· article· en· W2040099536 on OpenAlexvenueno aff
Hawrra Hassan Abass, Firas Mahdi Muhsin Al-Salbi

Bibliographic record

VenueComputer and Information Science · 2011
Typearticle
Languageen
FieldComputer Science
TopicImage Retrieval and Classification Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceRotation (mathematics)Artificial intelligenceMATLABScalingComputer visionOrientation (vector space)Invariant (physics)Position (finance)Pattern recognition (psychology)Computer graphics (images)GeometryMathematics

Abstract

fetched live from OpenAlex

An important aspect of human visual system is the ability to recognize objects despite of the change in their position, size, and orientation. Therefore, any pattern recognition system for many applications, such as for industry or military, must have the ability to recognize geometrically distorted patterns, i.e. scaled, rotated and shifted patterns. In this paper Hotelling transform algorithm has been used for scale and rotation invariant image processing, with the help of MATLAB-v10 simulation program, this algorithm has been implemented and tested on many images with black and white format of different scales and rotation angles.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0110.006

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 head0.037
GPT teacher head0.264
Teacher spread0.228 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueComputer and Information ScienceSame topicImage Retrieval and Classification TechniquesFrench-language works237,207