{"id":"W4412658509","doi":"10.1016/j.infrared.2025.106022","title":"Feature enhancement of local self-similarity in multi-modal image matching","year":2025,"lang":"en","type":"article","venue":"Infrared Physics & Technology","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Natural Science Basic Research Program of Shaanxi Province; China Scholarship Council; Northwest University; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Pattern recognition (psychology); Modal; Similarity (geometry); Image (mathematics); Feature (linguistics); Artificial intelligence; Matching (statistics); Computer science; Self-similarity; Image matching; Feature matching; Image enhancement; Computer vision; Mathematics; Materials science; Statistics; Geometry","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0006495917,0.0002697691,0.0005285889,0.0008607309,0.0002436102,0.0004368709,0.0006083176,0.0005494601,0.001340015],"category_scores_gemma":[0.001514709,0.0001910307,0.0005883771,0.0008335647,0.0002912956,0.001078185,0.0009266095,0.0003282737,0.0003886494],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002191863,"about_ca_system_score_gemma":0.0002659284,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005599225,"about_ca_topic_score_gemma":0.0008240878,"domain_scores_codex":[0.9995466,0.00007626807,0.00003156213,0.0000821662,0.0002137698,0.00004966769],"domain_scores_gemma":[0.9993395,0.0001531493,0.00007404974,0.0001196737,0.0002786138,0.00003487876],"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":[0.0006731484,0.0002700737,0.002387508,0.0002589035,0.0001351007,0.000122039,0.000149781,0.03331551,0.3990827,0.006322279,0.001643537,0.5556394],"study_design_scores_gemma":[0.00002211543,0.000228994,0.005433901,0.00001280627,0.00009352806,0.0004027496,0.00004647511,0.8571626,0.1319327,0.002287263,0.002345355,0.00003153565],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1142284,0.0004760487,0.8831046,0.00006759332,0.00006319019,0.00005352367,0.00005572284,0.0004213968,0.001529626],"genre_scores_gemma":[0.756241,0.0003473503,0.2403305,0.00008514556,0.00006007193,0.00005881976,0.0001931635,0.0001076721,0.002576335],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001340015,"threshold_uncertainty_score":0.004482746,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009275622031899161,"score_gpt":0.2943364484386982,"score_spread":0.2850608264067991,"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."}}