{"id":"W2121085532","doi":"10.1109/icinfa.2009.5204989","title":"Image-Based Visual Servoing using improved image moments","year":2009,"lang":"en","type":"article","venue":"","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Visual servoing; Artificial intelligence; Computer vision; Image moment; Jacobian matrix and determinant; Computer science; Image (mathematics); Preprocessor; Feature detection (computer vision); Moment (physics); Image processing; 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":[0.0001411729,0.000151509,0.0001357162,0.0001182295,0.0001658321,0.0003138673,0.0004497592,0.00002481895,0.00004327638],"category_scores_gemma":[0.00002898542,0.0001354077,0.00006386647,0.0003482091,0.00002430726,0.001547945,0.0001211577,0.0001092318,0.00005272887],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006207822,"about_ca_system_score_gemma":0.00005751439,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001153678,"about_ca_topic_score_gemma":3.791105e-7,"domain_scores_codex":[0.9988121,0.0000282948,0.0002090749,0.0003789864,0.0002061564,0.000365431],"domain_scores_gemma":[0.9993339,0.00002507401,0.00007323956,0.000354878,0.00009156009,0.0001213458],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00000545786,0.00008420089,0.00003497447,0.000004990362,0.000002529773,0.00001751247,0.00004645502,0.00007844897,0.9151574,0.0003211549,0.0001374862,0.08410943],"study_design_scores_gemma":[0.0005036481,0.00006156866,0.0003031743,0.00001828418,0.000001951953,0.000005403641,0.0000188862,0.9222485,0.07584898,0.0005875748,0.0002234339,0.0001786058],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01161012,0.00001557874,0.9843049,0.001005643,0.0001421517,0.0001071817,3.365357e-7,0.0003181613,0.002495936],"genre_scores_gemma":[0.1589829,9.447093e-7,0.8375277,0.00320931,0.00003294623,9.855106e-7,0.000001003576,0.000007958997,0.0002362947],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.92217,"threshold_uncertainty_score":0.5521764,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01400111239943888,"score_gpt":0.3218626289251476,"score_spread":0.3078615165257086,"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."}}