{"id":"W2548736919","doi":"10.1109/ica.2013.6734047","title":"Hand gesture recognition using convexity hull defects to control an industrial robot","year":2013,"lang":"en","type":"article","venue":"","topic":"Robot Manipulation and Learning","field":"Engineering","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"Sheridan College","funders":"","keywords":"Convex hull; Gesture recognition; Computer science; Artificial intelligence; Gesture; Hull; Robot; Computer vision; Industrial robot; Robot end effector; Noise (video); Controller (irrigation); Engineering; Regular polygon; Image (mathematics); 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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00009071108,0.0001128647,0.000130428,0.00007643696,0.00008752779,0.0001487899,0.00005077057,0.0001372721,0.001297943],"category_scores_gemma":[0.00005332329,0.0001096984,0.0000288524,0.0001133938,0.0000103599,0.000296458,0.000008030892,0.0002030619,0.000406143],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003992758,"about_ca_system_score_gemma":0.000007939786,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002051342,"about_ca_topic_score_gemma":0.00005428504,"domain_scores_codex":[0.9993951,0.00004810351,0.0001460182,0.0001302488,0.00009288932,0.0001876649],"domain_scores_gemma":[0.9996414,0.00003244703,0.00001958496,0.0001052788,0.00005518022,0.000146087],"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":[0.00001054738,0.00001287364,0.003735071,0.000008766044,0.0000197309,0.000001381657,0.0002179883,0.9478488,0.03506996,0.00002479682,0.0009763935,0.01207368],"study_design_scores_gemma":[0.001353751,0.00007198726,0.02378151,0.00002718075,0.00002357135,0.000006011652,0.0001199605,0.9701754,0.003441966,0.0001158177,0.0005753242,0.0003074559],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7611969,0.000009666604,0.2362094,0.00007116242,0.0003269691,0.000346126,5.095587e-7,0.0002525507,0.001586772],"genre_scores_gemma":[0.9966266,4.411416e-7,0.002611211,0.0003215768,0.0003169424,0.00001360369,0.00001488632,0.00002447467,0.00007032771],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2354297,"threshold_uncertainty_score":0.999615,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08363859063558648,"score_gpt":0.2455288249787256,"score_spread":0.1618902343431391,"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."}}