{"id":"W2155482448","doi":"10.1109/robio.2009.4913041","title":"Re-mapping of visual saliency in overt attention: A particle filter approach for robotic systems","year":2009,"lang":"en","type":"article","venue":"","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland; University of Waterloo","funders":"","keywords":"Particle filter; Saliency map; Visual attention; Computer vision; Artificial intelligence; Inhibition of return; Computer science; Salient; Process (computing); Eye tracking; Filter (signal processing); Set (abstract data type); Representation (politics); Psychology; Perception; Neuroscience","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":[],"consensus_categories":[],"category_scores_codex":[0.0003874731,0.00009914708,0.0001883219,0.0001242531,0.00005951344,0.00008000126,0.0002468354,0.00005223251,0.000006938167],"category_scores_gemma":[0.00002549185,0.00008747853,0.00009276591,0.000565393,0.00001573158,0.0004501988,0.00003605566,0.00005491922,0.000007138867],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003650815,"about_ca_system_score_gemma":0.00001815248,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002886974,"about_ca_topic_score_gemma":0.000004652646,"domain_scores_codex":[0.9987388,0.00006437714,0.0004169911,0.0003152156,0.000221082,0.0002435459],"domain_scores_gemma":[0.9995195,0.00002829668,0.00009726247,0.0002268796,0.0000753086,0.00005272744],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001933787,0.007684671,0.03761143,0.001012389,0.0001100699,0.00001782487,0.005634379,0.0800321,0.1708803,0.6190514,0.003219036,0.07455303],"study_design_scores_gemma":[0.0006143225,0.0003755658,0.01971578,0.00003562283,0.000003327283,0.000005341254,0.0003919807,0.977045,0.001181553,0.0004440676,0.00005404817,0.0001333724],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07297118,0.0000406278,0.9242096,0.0002580998,0.0002215844,0.0003603327,2.275613e-7,0.00009056261,0.001847779],"genre_scores_gemma":[0.9883183,0.000002013267,0.01086354,0.0001430951,0.00003402723,0.00003502968,0.000002444367,0.000003850793,0.0005977238],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9153471,"threshold_uncertainty_score":0.3567271,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0406909214617542,"score_gpt":0.2950803617643841,"score_spread":0.2543894403026299,"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."}}