{"id":"W2546161416","doi":"10.1109/icieca.2005.1644373","title":"Correspondence Method for Registration of Range Images Using Evolutionary Algorithms","year":2006,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Correspondence problem; Computer science; Image registration; Artificial intelligence; Viewpoints; Range (aeronautics); Algorithm; Transformation (genetics); Computer vision; Set (abstract data type); Evolutionary algorithm; Process (computing); Genetic algorithm; Image (mathematics); Pattern recognition (psychology); Machine learning","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":[0.0014153,0.0006140211,0.001013276,0.00190806,0.0006595385,0.0009376241,0.001460599,0.001465529,0.003581123],"category_scores_gemma":[0.003517124,0.0004681056,0.001129016,0.001709514,0.0008787439,0.001281373,0.001184396,0.00117113,0.00115721],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006272314,"about_ca_system_score_gemma":0.000789758,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009883817,"about_ca_topic_score_gemma":0.000945952,"domain_scores_codex":[0.9985538,0.0004071199,0.00005978431,0.0002415904,0.0006786635,0.00005913668],"domain_scores_gemma":[0.9992636,0.0003600773,0.00006834605,0.0001014649,0.000180057,0.0000263224],"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":[0.0001388201,0.000175455,0.0006125878,0.0002085711,0.0001576296,0.0002273134,0.0002975553,0.3029548,0.02863967,0.143836,0.003164199,0.5195873],"study_design_scores_gemma":[0.00003219156,0.00007283833,0.0003156181,0.00001651435,0.00002416336,0.0001800933,0.00002857645,0.9623102,0.008150865,0.02090967,0.007923413,0.00003575996],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001258805,0.00005027514,0.997768,0.00002038048,0.00001470476,0.00002292114,0.00000650354,0.000129195,0.0007291838],"genre_scores_gemma":[0.04685474,0.0001179076,0.9501277,0.00003072071,0.00002880741,0.0001766766,0.00005392357,0.0001199151,0.002489584],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003581123,"threshold_uncertainty_score":0.01198012,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01906843052282963,"score_gpt":0.2707842852822135,"score_spread":0.2517158547593839,"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."}}