{"id":"W2168380116","doi":"10.1109/have.2006.283776","title":"3D Model Creation Using Self-Identifying Markers and SIFT Keypoints","year":2006,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Scale-invariant feature transform; Fiducial marker; Artificial intelligence; Computer vision; Computer science; RANSAC; Object (grammar); Pose; Feature (linguistics); USable; Feature extraction; Delaunay triangulation; Object detection; Set (abstract data type); Pattern recognition (psychology); Image (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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003670405,0.0009585059,0.0008488884,0.001218804,0.0004799123,0.001465654,0.001228017,0.0009435505,0.0076858],"category_scores_gemma":[0.001174975,0.001055793,0.001673775,0.0007540718,0.0007207337,0.001184349,0.001872132,0.0008466383,0.004763293],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006248281,"about_ca_system_score_gemma":0.001007047,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002986795,"about_ca_topic_score_gemma":0.004202208,"domain_scores_codex":[0.9994518,0.00003643213,0.00002640722,0.0001113984,0.0003454123,0.00002852879],"domain_scores_gemma":[0.9993773,0.000102739,0.0000481172,0.0003221494,0.0001104584,0.00003926088],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000286619,0.0001230104,0.002142155,0.0004410475,0.0001269657,0.0007506687,0.0007438033,0.1914842,0.2181264,0.02907278,0.01653421,0.5401682],"study_design_scores_gemma":[0.0000366592,0.0001620048,0.001146985,0.00004412721,0.0000428551,0.001106709,0.0001376585,0.8081336,0.126956,0.006454465,0.05566157,0.0001172938],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003493727,0.00003358171,0.9894231,0.00003296455,0.00002921506,0.00006178649,0.0001665396,0.005289887,0.001469233],"genre_scores_gemma":[0.1067425,0.0002118203,0.886227,0.00004346354,0.00001194961,0.0001865679,0.00115215,0.001062367,0.004362244],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0076858,"threshold_uncertainty_score":0.0257116,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01178574676519713,"score_gpt":0.2135773390977608,"score_spread":0.2017915923325637,"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."}}