{"id":"W2163751734","doi":"10.1109/igarss.1989.567162","title":"Edge Following As Graph Searching And Hough Transform Algorithms For Linement Detection","year":2005,"lang":"en","type":"article","venue":"","topic":"Image and Object Detection Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Hough transform; Computer science; Enhanced Data Rates for GSM Evolution; Edge detection; Artificial intelligence; Graph; Algorithm; Computer vision; Pattern recognition (psychology); Theoretical computer science; Image processing; Image (mathematics)","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":[0.0003588079,0.00009952064,0.00009408633,0.000156383,0.000258435,0.000169201,0.0001720479,0.00004509794,0.000004649375],"category_scores_gemma":[0.00001280675,0.00008831242,0.0001046836,0.0001955945,0.00001237364,0.0006867218,0.0000404936,0.00009081838,0.000005080204],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003693868,"about_ca_system_score_gemma":0.00001771888,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007078196,"about_ca_topic_score_gemma":0.00005929263,"domain_scores_codex":[0.9992027,0.00001608083,0.0001520117,0.0002578053,0.000149904,0.0002215376],"domain_scores_gemma":[0.999683,0.00004287299,0.00002210333,0.0001571542,0.00003525322,0.00005956677],"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":[0.000003567684,0.00001445245,5.616543e-7,0.00000669433,0.000009798434,0.000001089555,0.0003860866,0.000002596458,0.003794527,0.0001746355,0.00006238405,0.9955436],"study_design_scores_gemma":[0.0003503032,0.0002638426,0.00002435357,0.000009086065,0.000006468615,0.00001654117,0.00006532035,0.01303729,0.9672509,0.007458851,0.01137849,0.0001385619],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003732397,0.00008142494,0.9916652,0.0008282525,0.0001528564,0.0003716019,4.545521e-7,0.0004959059,0.002671887],"genre_scores_gemma":[0.7312743,0.00004242555,0.2671789,0.0005639042,0.0001552654,0.00009732742,6.262765e-7,0.00001020972,0.0006770806],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.995405,"threshold_uncertainty_score":0.3601276,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01401422444210799,"score_gpt":0.2855552386484075,"score_spread":0.2715410142062995,"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."}}