{"id":"W2171248187","doi":"10.1109/ccece.2007.366","title":"Scale-Space Feature Detection for Close Range Camera Calibration","year":2007,"lang":"en","type":"article","venue":"","topic":"Optical measurement and interference techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer vision; Artificial intelligence; Computer science; Focus (optics); Calibration; Scale space; Camera resectioning; Feature (linguistics); Feature extraction; Scale (ratio); Object detection; Feature detection (computer vision); Field (mathematics); Camera auto-calibration; Image (mathematics); Image processing; Pattern recognition (psychology); Mathematics; Geography; Optics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003099372,0.0003798999,0.0003791779,0.0005494336,0.000283599,0.0004207745,0.000659219,0.0005289514,0.002282975],"category_scores_gemma":[0.001740024,0.0002232022,0.0002534733,0.0007038212,0.0003754112,0.0008161717,0.0004416403,0.0007065641,0.00106033],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003096136,"about_ca_system_score_gemma":0.0002194598,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000731042,"about_ca_topic_score_gemma":0.001062731,"domain_scores_codex":[0.9996135,0.00004800041,0.00001181734,0.00007174473,0.0002266197,0.00002833718],"domain_scores_gemma":[0.9993054,0.0002494769,0.00006760431,0.000126929,0.0002186627,0.00003182126],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001789005,0.0001200143,0.0009835038,0.00008583113,0.00002577246,0.0001391147,0.0001279765,0.027574,0.2745709,0.009441726,0.002657606,0.6840948],"study_design_scores_gemma":[0.00003031984,0.0001564222,0.002660604,0.00001226558,0.00001716471,0.0004884862,0.00003380987,0.8269115,0.1561034,0.005589168,0.007960858,0.00003595851],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01938924,0.0001502118,0.9780468,0.00005864976,0.00002366571,0.00002297064,0.00002020204,0.001111296,0.001176887],"genre_scores_gemma":[0.3057635,0.0001925966,0.6919549,0.00005703613,0.00003154767,0.00004891174,0.0001034647,0.0001140812,0.001734007],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002282975,"threshold_uncertainty_score":0.007637322,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02341861378960484,"score_gpt":0.2730002805957424,"score_spread":0.2495816668061376,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}