{"id":"W2019873548","doi":"10.2316/journal.206.2005.2.206-2781","title":"Colour Histogram Algorithms for Visual Robot Control","year":2005,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Computer vision; Histogram; Color histogram; Histogram matching; Color normalization; Computer science; Video tracking; Object (grammar); Visual servoing; Adaptive histogram equalization; Robot; Histogram equalization; Pattern recognition (psychology); Color image; Image (mathematics); Image processing","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004151925,0.0006416506,0.0008649877,0.001182225,0.0002914968,0.001321261,0.0009038467,0.0008235128,0.01303608],"category_scores_gemma":[0.001568782,0.0002928494,0.0003714644,0.001766633,0.0006413247,0.001239811,0.0006294397,0.001060015,0.002894976],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006516535,"about_ca_system_score_gemma":0.0004320415,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004017799,"about_ca_topic_score_gemma":0.002756508,"domain_scores_codex":[0.9997397,0.0000533727,0.00001084934,0.0000647355,0.0001042675,0.00002700166],"domain_scores_gemma":[0.9993749,0.0001705642,0.00003171649,0.00008857179,0.0003052984,0.00002894401],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000184812,0.0000630949,0.0002277704,0.0002199966,0.00004622218,0.00003366156,0.0000353448,0.05730739,0.01787366,0.05017651,0.01997511,0.8538566],"study_design_scores_gemma":[0.00004542687,0.00007242079,0.001255028,0.00006241603,0.00003118182,0.0001027216,0.00003057638,0.8679273,0.01437979,0.08536789,0.03067658,0.00004874098],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002889114,0.003276325,0.9875103,0.0002847472,0.0004661179,0.00003529178,0.00007444796,0.001221034,0.004242676],"genre_scores_gemma":[0.261458,0.007227216,0.6876862,0.0005142788,0.001090977,0.0001841629,0.0005180596,0.000894296,0.04042688],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01303608,"threshold_uncertainty_score":0.04360998,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01323829649491944,"score_gpt":0.3233278162896373,"score_spread":0.3100895197947179,"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."}}