{"id":"W2072183894","doi":"10.1117/1.1543158","title":"Invariant object recognition under three-dimensional rotations and changes of scale","year":2003,"lang":"en","type":"article","venue":"Optical Engineering","topic":"Image and Object Detection Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Invariant (physics); Artificial intelligence; Cognitive neuroscience of visual object recognition; Centroid; Computer vision; Computer science; 3D single-object recognition; Scale space; Rotation (mathematics); Scale invariance; Pattern recognition (psychology); Classifier (UML); Feature extraction; Mathematics; Image processing","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":[0.0001256007,0.00006136845,0.00007759068,0.00008529137,0.00003253255,0.00002726273,0.00005632659,0.00003815258,0.00001007501],"category_scores_gemma":[0.00006462281,0.00005954289,0.00001759305,0.0001702126,0.0000169369,0.0001413287,0.0000299114,0.00007135809,0.000003897072],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001258263,"about_ca_system_score_gemma":0.00001396161,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008263735,"about_ca_topic_score_gemma":0.00001072992,"domain_scores_codex":[0.9995825,0.000007455348,0.00008804413,0.0001244214,0.00009171072,0.0001058997],"domain_scores_gemma":[0.9997236,0.00006886889,0.00001642862,0.0001039871,0.00004465529,0.00004250279],"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":[0.00001496222,0.00025517,0.00009080095,0.0002198701,0.00009870963,0.00003561808,0.0005386858,0.00820398,0.461549,0.238549,0.0001296991,0.2903146],"study_design_scores_gemma":[0.0001819688,0.0001186689,0.001602342,0.00005487882,0.00000888369,0.00006811436,0.00001057604,0.08826176,0.9033603,0.006047407,0.0001110283,0.0001740378],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03767671,0.00005628516,0.961179,0.0001262391,0.00007335921,0.00007169916,8.654274e-7,0.0001233954,0.0006924599],"genre_scores_gemma":[0.7232713,0.000005985842,0.276627,0.00004951251,0.00001297898,0.0000141736,7.345842e-7,0.000005615288,0.00001270582],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6855946,"threshold_uncertainty_score":0.2428088,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01248379252825754,"score_gpt":0.2071626655387065,"score_spread":0.1946788730104489,"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."}}