{"id":"W2272596542","doi":"10.1007/s00371-015-1184-x","title":"Mesh saliency detection via double absorbing Markov chain in feature space","year":2015,"lang":"en","type":"article","venue":"The Visual Computer","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"National Natural Science Foundation of China","keywords":"Markov chain; Computer science; Laplacian smoothing; Blob detection; Feature (linguistics); Laplace operator; Artificial intelligence; Pattern recognition (psychology); Absorbing Markov chain; Salient; Partition (number theory); Algorithm; Smoothing; Feature vector; Computer vision; Mathematics; Image (mathematics); Markov model; Edge detection; Image processing; Variable-order Markov model; Combinatorics; Mesh generation","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":[0.0007490655,0.0003514961,0.0009607542,0.001328764,0.0004607016,0.0008875542,0.00131359,0.001059077,0.002140329],"category_scores_gemma":[0.004889733,0.0005037202,0.0005896147,0.0008139656,0.0008511356,0.001612524,0.001389886,0.0008595179,0.0002494106],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008950428,"about_ca_system_score_gemma":0.0006710117,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005054794,"about_ca_topic_score_gemma":0.004774935,"domain_scores_codex":[0.9996508,0.00007048757,0.00001459412,0.0001010645,0.00009060533,0.00007249975],"domain_scores_gemma":[0.9969022,0.002212096,0.000241094,0.0002097008,0.0002943908,0.0001405535],"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":[0.0007848643,0.0002015012,0.008132861,0.0002561113,0.0001579394,0.000489032,0.0003410716,0.6367301,0.03819279,0.1252238,0.002891411,0.1865985],"study_design_scores_gemma":[0.00000387926,0.000009852034,0.0002906036,0.000002058983,0.00000305851,0.00001450956,0.000003735953,0.9898094,0.0008288408,0.008965416,0.00006497074,0.000003748565],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06516855,0.0001246509,0.9334472,0.0001299359,0.0000230581,0.00002100583,0.00005734792,0.000249605,0.0007786523],"genre_scores_gemma":[0.9076855,0.00009586025,0.09032075,0.000069306,0.00004119864,0.00004844988,0.0001428645,0.00006019538,0.00153587],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005054794,"threshold_uncertainty_score":0.01005077,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02308774106452338,"score_gpt":0.2819964231903871,"score_spread":0.2589086821258637,"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."}}