{"id":"W2133058885","doi":"10.1109/ccece.1999.808095","title":"Subspace-based line and curve extraction from noisy images","year":2003,"lang":"en","type":"article","venue":"","topic":"Image and Object Detection Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Subspace topology; Offset (computer science); Computer science; Artificial intelligence; Computer vision; Parametric statistics; Tracking (education); Parametric equation; Parametric model; Feature extraction; Line (geometry); Algorithm; Mathematics; Geometry","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0004196985,0.0006892518,0.00109392,0.001587229,0.0002963651,0.0007063635,0.0008569762,0.0007042271,0.001467809],"category_scores_gemma":[0.001448797,0.0003665371,0.0006970971,0.00206149,0.0004396692,0.001634326,0.0005969956,0.0006389031,0.001552932],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002793199,"about_ca_system_score_gemma":0.0004086551,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009635221,"about_ca_topic_score_gemma":0.001565508,"domain_scores_codex":[0.999482,0.00007779452,0.00002918956,0.0001441226,0.000221204,0.00004571459],"domain_scores_gemma":[0.9993196,0.0001884205,0.0001063572,0.0001616971,0.0001955438,0.00002848584],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002269726,0.00004536478,0.001037703,0.0001742222,0.0000646902,0.0001481166,0.00009530016,0.0537147,0.1507399,0.003780305,0.001415765,0.7885569],"study_design_scores_gemma":[0.00001347978,0.0001053988,0.002307042,0.00001315068,0.00002918408,0.0003641488,0.00005968811,0.8945597,0.0906485,0.005745863,0.006107675,0.00004610906],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009064094,0.0001422681,0.9897279,0.00002373868,0.00001177572,0.00001633041,0.00005708832,0.0006939916,0.0002628957],"genre_scores_gemma":[0.09654258,0.0005883517,0.9003749,0.0000326664,0.00003609918,0.0000625113,0.0005379746,0.0001614974,0.0016634],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001587229,"threshold_uncertainty_score":0.00491035,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01020822695422201,"score_gpt":0.2509224207470986,"score_spread":0.2407141937928766,"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."}}