{"id":"W2085490484","doi":"10.1117/12.766703","title":"Hyperspectral image visualization based on a human visual model","year":2008,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Cégep de Lévis","funders":"","keywords":"Artificial intelligence; Hyperspectral imaging; Computer science; Computer vision; Visualization; Hue; Human visual system model; Feature (linguistics); Image resolution; Pattern recognition (psychology); Salient; Image (mathematics); Contrast (vision)","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004369198,0.0002824947,0.0002985422,0.0001856523,0.000220935,0.0001379809,0.001029151,0.0001509708,0.000007157104],"category_scores_gemma":[0.0002227605,0.0002462118,0.000576727,0.0005013958,0.0001833514,0.0008738356,0.0001248264,0.000243056,0.000003268656],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001749795,"about_ca_system_score_gemma":0.00004497657,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000531061,"about_ca_topic_score_gemma":7.563332e-8,"domain_scores_codex":[0.9976929,2.431607e-8,0.0005683849,0.0004739653,0.0009103869,0.0003543276],"domain_scores_gemma":[0.998134,0.00005469056,0.0003060791,0.00007179845,0.00131235,0.0001211199],"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":[0.00003067745,0.0002210954,0.00008169605,0.00008461255,0.000055078,1.197839e-7,0.0001746624,0.0009289345,0.4519317,0.545545,0.0008801273,0.0000663238],"study_design_scores_gemma":[0.0008119273,0.0005169142,0.000548583,0.0000863067,0.00002763829,0.00001218289,0.0001555347,0.8433447,0.1524479,0.00166195,0.0001327701,0.0002535806],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9836668,0.000008899915,0.01206159,0.0010126,0.0001758581,0.0003598017,0.000008678981,0.0002048303,0.002500951],"genre_scores_gemma":[0.8693084,0.00001495957,0.1298661,0.0002424224,0.0001949465,0.00008486816,0.000006324925,0.00004003206,0.0002419989],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8424158,"threshold_uncertainty_score":0.999999,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02063678434308865,"score_gpt":0.2736048832583567,"score_spread":0.2529680989152681,"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."}}