{"id":"W2102472022","doi":"10.1109/pacrim.1993.407178","title":"Hierarchical clustering for automated line detection","year":2002,"lang":"en","type":"article","venue":"","topic":"Image and Object Detection Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Subpixel rendering; Contiguity; Line (geometry); Principal component analysis; Artificial intelligence; Pixel; Computer science; Pattern recognition (psychology); Similarity (geometry); Cluster analysis; Line segment; Segmentation; Image segmentation; Measure (data warehouse); Hierarchical clustering; Computer vision; Mathematics; Image (mathematics); Data mining; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001041028,0.00006744063,0.00006908482,0.0001054677,0.0001165817,0.00009642601,0.0002121021,0.00004976077,0.00002580212],"category_scores_gemma":[0.0000322796,0.0000595155,0.00004967154,0.000207002,0.00001167914,0.0002835423,0.00006876476,0.0000665901,0.00003704957],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002619117,"about_ca_system_score_gemma":0.000002955525,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001145034,"about_ca_topic_score_gemma":0.00001602903,"domain_scores_codex":[0.9994317,0.00001709355,0.0001252202,0.0001868121,0.00008035252,0.0001587634],"domain_scores_gemma":[0.9996414,0.00004048194,0.00002435345,0.0002079155,0.00004717855,0.00003872745],"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":[0.000004143172,0.00003238266,8.07044e-7,0.00001100732,0.000005182457,0.000002768234,0.0001014217,0.00002788203,0.01891662,0.0002874008,0.001977547,0.9786328],"study_design_scores_gemma":[0.0001002117,0.0001241819,0.00001911122,0.000002218389,8.794036e-7,0.00001807641,0.000001361526,0.6527724,0.3428573,0.0004619607,0.00358306,0.00005921919],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0004406728,0.00001427164,0.9915151,0.0003799059,0.0001862203,0.0001581339,3.172206e-7,0.0043914,0.002914001],"genre_scores_gemma":[0.8010138,0.000006430889,0.1971005,0.0004413566,0.00008164993,0.00005033324,2.927598e-7,0.000006847277,0.001298802],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9785736,"threshold_uncertainty_score":0.2426971,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02105417559622596,"score_gpt":0.2613898753875706,"score_spread":0.2403356997913446,"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."}}