{"id":"W1987451068","doi":"10.1117/12.2082860","title":"Multi-polarimetric textural distinctiveness for outdoor robotic saliency detection","year":2015,"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":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Artificial intelligence; Computer science; Computer vision; Polarimetry; Optimal distinctiveness theory; Object detection; Pattern recognition (psychology); Leverage (statistics); Pixel; Mobile robot; Invariant (physics); Robot; Mathematics; Optics","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":[0.000305544,0.0003794466,0.0004061849,0.001284336,0.00017679,0.0004072832,0.0004623829,0.0003200687,0.001003857],"category_scores_gemma":[0.001514408,0.0001836323,0.0004406356,0.0005645268,0.0003731611,0.0006738873,0.0005830789,0.0003768913,0.0002269698],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003724202,"about_ca_system_score_gemma":0.0002033802,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00120234,"about_ca_topic_score_gemma":0.001615872,"domain_scores_codex":[0.9998605,0.00002439379,0.000005174566,0.00003582729,0.0000504705,0.00002368966],"domain_scores_gemma":[0.9994765,0.0001896694,0.000102816,0.00006382645,0.0001214809,0.00004570148],"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":[0.0005567338,0.0001471288,0.004895972,0.0002977641,0.0001108859,0.0002803228,0.0002525508,0.1337399,0.4125921,0.009502981,0.001743476,0.4358802],"study_design_scores_gemma":[0.000009334988,0.0001063926,0.007303568,0.000009593728,0.00002085562,0.0002376175,0.00004189377,0.9583254,0.02667162,0.006487514,0.0007665458,0.00001963138],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1052937,0.0001719656,0.8928735,0.00007987864,0.00001712981,0.00004154829,0.0001008981,0.0004217415,0.0009996135],"genre_scores_gemma":[0.8590153,0.0001430946,0.1399065,0.0000418431,0.00004062556,0.00003721358,0.0001748973,0.00006179671,0.0005786351],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001284336,"threshold_uncertainty_score":0.003358245,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02708150240129626,"score_gpt":0.2658855449303761,"score_spread":0.2388040425290799,"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."}}