{"id":"W3115934669","doi":"10.1109/jstars.2020.3046838","title":"Unsupervised Change Detection Using Multiscale and Multiresolution Gaussian-Mixture-Model Guided by Saliency Enhancement","year":2020,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Key Science and Technology Program of Shaanxi Province; National Natural Science Foundation of China; Royal Society","keywords":"Computer science; Artificial intelligence; Mixture model; Gaussian process; Pattern recognition (psychology); Change detection; Gaussian; Multiresolution analysis; Computer vision; Wavelet; Wavelet transform; Physics","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":[0.0001141123,0.0001522765,0.0002227053,0.0001184139,0.0001072561,0.00003407212,0.00004436024,0.0001108857,0.000001871438],"category_scores_gemma":[0.0000434135,0.0001537304,0.0000218967,0.000417924,0.00003205454,0.0001854066,0.0000163328,0.000322933,1.780341e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006810411,"about_ca_system_score_gemma":0.00002096671,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002687286,"about_ca_topic_score_gemma":0.00003540284,"domain_scores_codex":[0.999029,0.00002108321,0.0004456207,0.0001468125,0.0001734279,0.0001840194],"domain_scores_gemma":[0.9995332,0.00002184864,0.0001283306,0.00007291804,0.0001537284,0.00008991181],"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":[0.00001244854,0.000007044051,0.00002021401,0.00004485436,0.000009515183,0.00000302529,0.0005885367,0.003633738,0.8668054,0.000002813398,0.00002922045,0.1288432],"study_design_scores_gemma":[0.0004247598,0.00003241642,0.0006395779,0.00008580842,0.00001478865,0.00002088454,0.00004305294,0.7937251,0.2044362,0.0001483993,0.000295154,0.0001338035],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4641025,0.0002530948,0.5351605,0.0001709266,0.00005990967,0.0001666997,0.000002306561,0.00004799265,0.00003605298],"genre_scores_gemma":[0.6707465,0.0007690054,0.3281851,0.0001380588,0.0001303363,2.429981e-7,0.000002611582,0.00002034052,0.000007821927],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7900914,"threshold_uncertainty_score":0.6268944,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04549166980485922,"score_gpt":0.2552681937407432,"score_spread":0.2097765239358839,"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."}}