{"id":"W2049479249","doi":"10.1109/icra.2014.6907582","title":"Viewpoint selection for vision systems in industrial inspection","year":2014,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada; Consejo Nacional de Ciencia y Tecnología","keywords":"Viewpoints; Computer science; Artificial intelligence; Computer vision; Process (computing); Greedy algorithm; Graph; Task (project management); Machine vision; Object (grammar); Visual inspection; Selection (genetic algorithm); CAD; Engineering drawing; Algorithm; Theoretical computer science; Engineering; Systems engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001775018,0.00006371867,0.00009194711,0.0001064929,0.00003041682,0.00003230208,0.0000220186,0.00009971247,0.000004550068],"category_scores_gemma":[0.00003295593,0.00006077698,0.00001997628,0.0001651792,0.000003540798,0.00006856213,0.000002408204,0.00006485876,0.000008559866],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009574866,"about_ca_system_score_gemma":0.000005323036,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001194041,"about_ca_topic_score_gemma":0.00009481063,"domain_scores_codex":[0.9995517,0.000021212,0.0001757311,0.00008814952,0.00006093297,0.0001022634],"domain_scores_gemma":[0.9998509,0.00002993931,0.00001518027,0.00005199118,0.00002864155,0.00002332992],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000007925058,0.00001020199,0.0002793795,0.00002926876,0.000002918442,2.901447e-8,0.00001597888,0.9830154,0.00293541,0.007396307,0.002264473,0.004042774],"study_design_scores_gemma":[0.0004821749,0.00008042434,0.000263191,0.00002623328,0.000003136327,9.415972e-7,0.00001395597,0.9885565,0.001950826,0.0001395389,0.008406612,0.00007643826],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05869451,0.0000216307,0.9375419,0.00007571709,0.001028472,0.0003391723,6.57122e-7,0.0003189745,0.001978956],"genre_scores_gemma":[0.9991221,0.000009143849,0.0004435951,0.00002256472,0.0002876664,0.00002046464,0.00001408647,0.00001660929,0.00006376238],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9404276,"threshold_uncertainty_score":0.2478413,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0164826156304021,"score_gpt":0.2206086929270828,"score_spread":0.2041260772966807,"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."}}