{"id":"W2083904196","doi":"10.1117/12.381623","title":"<title>Adaptive hill climbing and iterative closest point algorithm for multisensor image registration with partial Hausdorff distance</title>","year":2000,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lockheed Martin (Canada)","funders":"","keywords":"Iterative closest point; Hausdorff distance; Hill climbing; Outlier; Artificial intelligence; Algorithm; Computer science; Iterative method; Image registration; Matching (statistics); Pattern recognition (psychology); Point set registration; Feature (linguistics); Mathematics; Point (geometry); Computer vision; Image (mathematics); Point cloud","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":[0.0002227542,0.0001713194,0.0001920289,0.00003966525,0.00008083603,0.0001376087,0.0003281891,0.00006166675,0.00002532964],"category_scores_gemma":[0.0001005511,0.0001365,0.0001506229,0.0001326853,0.0001395446,0.0006410208,0.0000529472,0.000131056,0.000004537038],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000668236,"about_ca_system_score_gemma":0.00001925736,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":9.350129e-7,"about_ca_topic_score_gemma":6.46593e-8,"domain_scores_codex":[0.9989023,2.91336e-8,0.0002895924,0.0003055037,0.0002897427,0.000212864],"domain_scores_gemma":[0.9990688,0.00007374747,0.0001598177,0.00004677524,0.0005791858,0.0000716806],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007228227,0.0001053486,0.00001674533,0.0001258352,0.0001360973,5.020509e-7,0.0003795475,0.00004191093,0.08767647,0.843958,0.006090598,0.06139671],"study_design_scores_gemma":[0.0011138,0.0003722227,0.0001026663,0.0002789148,0.00004873272,0.00002855879,0.0002155137,0.9270672,0.02824175,0.002648696,0.03952309,0.0003589267],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.6216921,0.0004288821,0.3144993,0.005552172,0.0006161888,0.00193053,0.000188559,0.0003686496,0.05472368],"genre_scores_gemma":[0.04941529,0.0000973807,0.9488012,0.0001060017,0.0002758185,0.00007389702,0.000006910368,0.00003377707,0.001189674],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9270252,"threshold_uncertainty_score":0.5566308,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009270750371717271,"score_gpt":0.234885173965534,"score_spread":0.2256144235938168,"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."}}