{"id":"W2103717630","doi":"10.1109/tsmcb.2009.2038895","title":"An Object-Based Visual Attention Model for Robotic Applications","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":68,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Artificial intelligence; Gestalt psychology; Computer science; Coding (social sciences); Object (grammar); Computer vision; Perception; Cognitive neuroscience of visual object recognition; Pattern recognition (psychology); Visual processing; Top-down and bottom-up design; Psychology; 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":[0.0003192888,0.0006292454,0.0004902909,0.0004869853,0.0003353661,0.0008301795,0.001948074,0.0008495399,0.003520087],"category_scores_gemma":[0.0006498565,0.0002539617,0.0008510003,0.0003937784,0.0004742633,0.001470183,0.0008662464,0.0006843291,0.000766158],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009475157,"about_ca_system_score_gemma":0.0005981022,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005463914,"about_ca_topic_score_gemma":0.003259375,"domain_scores_codex":[0.9998048,0.00002714656,0.000008644104,0.00006741567,0.00006223785,0.00002972543],"domain_scores_gemma":[0.9998604,0.00003979908,0.00001685382,0.00002124679,0.00004551236,0.00001621484],"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":[0.00018413,0.000120829,0.001110404,0.0003520373,0.0001585169,0.0004422902,0.0003323771,0.4597727,0.03433574,0.3096833,0.007347013,0.1861607],"study_design_scores_gemma":[0.00001259037,0.00006007298,0.0004496393,0.000009365956,0.00002464182,0.00007797528,0.00001351604,0.9476674,0.001079532,0.04651981,0.004072057,0.00001342182],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01566438,0.001304029,0.9695513,0.0004759149,0.0001276811,0.00005760512,0.0001020723,0.0007337694,0.01198337],"genre_scores_gemma":[0.7777166,0.00180336,0.1968377,0.0004260511,0.0002389626,0.0003305908,0.0002955035,0.0001731363,0.02217811],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005463914,"threshold_uncertainty_score":0.01177591,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02103009153671805,"score_gpt":0.2822144131704359,"score_spread":0.2611843216337179,"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."}}