{"id":"W2985872997","doi":"10.1109/imtc.2005.1604348","title":"Non-Contact 3D Coordinates Measurement of Cross-Cutting Feature Points on the Surface of Large-Scale Workpiece Based on Machine Vision Method","year":2006,"lang":"en","type":"article","venue":"2005 IEEE Instrumentationand Measurement Technology Conference Proceedings","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"Harbin Institute of Technology","keywords":"Computer vision; Artificial intelligence; Feature (linguistics); Computer science; Monocular vision; Machine vision; Scale (ratio); Stereopsis; Coordinate-measuring machine; Point (geometry); Camera resectioning; Correctness; Surface (topology); Calibration; Stereoscopy; System of measurement; Mathematics; Engineering; Algorithm; Geometry; Mechanical engineering","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002438291,0.0003588779,0.0004628209,0.0003331302,0.0002216121,0.00007657599,0.0002973564,0.0003889619,0.00005285302],"category_scores_gemma":[0.0001317956,0.0002758334,0.0001129013,0.0006145354,0.00008974593,0.0001954761,0.00002513381,0.0005000225,0.000008957949],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003705137,"about_ca_system_score_gemma":0.00009126162,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005013345,"about_ca_topic_score_gemma":0.0000391842,"domain_scores_codex":[0.9971823,0.00003195717,0.0007020934,0.0003975706,0.001263983,0.0004221262],"domain_scores_gemma":[0.9980294,0.00005683992,0.0004229723,0.0002361737,0.001205976,0.00004862404],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004165326,0.0003980059,0.02958305,0.0003162608,0.0001512232,0.000001259368,0.0002054114,0.008464222,0.9429589,0.002185431,0.003760287,0.01155945],"study_design_scores_gemma":[0.002379207,0.0004891847,0.005255271,0.001061959,0.00004880583,0.0000040934,0.0003924642,0.06008408,0.9285968,0.0002950071,0.001102997,0.0002901147],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9718057,0.0001205266,0.01985532,0.001033622,0.0006463167,0.001342393,0.00004492218,0.0003035077,0.004847704],"genre_scores_gemma":[0.9967499,0.0000100588,0.002983394,0.00004361382,0.00005510627,0.00007399264,0.000006400049,0.00003619196,0.00004138763],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05161986,"threshold_uncertainty_score":0.9999694,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02043772412954692,"score_gpt":0.2691754428949657,"score_spread":0.2487377187654188,"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."}}