{"id":"W4221161295","doi":"10.3390/jimaging8040110","title":"Salient Object Detection by LTP Texture Characterization on Opposing Color Pairs under SLICO Superpixel Constraint","year":2022,"lang":"en","type":"article","venue":"Journal of Imaging","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Artificial intelligence; Computer vision; Pattern recognition (psychology); RGB color model; Computer science; Color space; Texture (cosmology); RGB color space; Mathematics; Local binary patterns; Color image; Image (mathematics); Image processing; Histogram","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":[0.0006959872,0.0001262703,0.0001582551,0.0002891275,0.00049281,0.0002318435,0.0002975434,0.00002462156,0.0000465173],"category_scores_gemma":[0.00003280341,0.0001180452,0.0001302531,0.0003978708,0.00003130086,0.000638883,0.00009253452,0.0004146312,0.000005711516],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003478358,"about_ca_system_score_gemma":0.00007065496,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005390308,"about_ca_topic_score_gemma":9.380142e-7,"domain_scores_codex":[0.9983755,0.0002373878,0.0004209168,0.0002071763,0.0005570838,0.0002019498],"domain_scores_gemma":[0.9991922,0.00003488194,0.0004028786,0.0001416803,0.0001360291,0.00009233403],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004722317,0.0001906446,0.0004191998,0.000007673729,0.00002496051,0.00003125516,0.0006655764,0.001128159,0.8665206,0.0009412862,0.0003904005,0.129633],"study_design_scores_gemma":[0.004447378,0.003359865,0.02622953,0.0002289921,0.0001045645,0.005297946,0.006479982,0.6933216,0.2211064,0.001497055,0.03665728,0.001269413],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3704834,0.00003952353,0.6252984,0.002169508,0.001658855,0.0001021495,0.000005000567,0.00005788246,0.0001853006],"genre_scores_gemma":[0.9973939,0.000008073078,0.000438284,0.001966969,0.0001031163,0.000005416378,0.000004527065,0.00001166652,0.00006805963],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6921934,"threshold_uncertainty_score":0.4813744,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009501098638695452,"score_gpt":0.2402653437524187,"score_spread":0.2307642451137233,"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."}}