{"id":"W2136001449","doi":"10.1109/tip.2011.2150235","title":"Generalized Random Walks for Fusion of Multi-Exposure Images","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":236,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Beihang University","keywords":"Image fusion; Artificial intelligence; Computer science; Probabilistic logic; Fusion; Computer vision; Image (mathematics); Image quality; Consistency (knowledge bases); Random walk; Random walker algorithm; Pattern recognition (psychology); Mathematics; Statistics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000343461,0.0002135874,0.0002734224,0.0002392468,0.0002705983,0.00009983799,0.0005708917,0.00008246017,0.00003804391],"category_scores_gemma":[0.0000128064,0.0001967015,0.0001590286,0.0003223741,0.0001067004,0.001185484,0.00000533321,0.0001544253,0.000006531116],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003516636,"about_ca_system_score_gemma":0.00007769632,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002730007,"about_ca_topic_score_gemma":0.000004933694,"domain_scores_codex":[0.9985878,0.00005819934,0.0004059118,0.000418071,0.0002313808,0.0002986736],"domain_scores_gemma":[0.9989386,0.00005254753,0.0001976441,0.0004177491,0.0003335812,0.00005992758],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001989703,0.0005325271,0.000003978724,0.0001992817,0.00002347019,0.000004603652,0.00178847,0.00003940672,0.6260961,0.00004063559,0.0001253687,0.3709472],"study_design_scores_gemma":[0.002216116,0.0001980673,0.00002994998,0.0001111207,0.00003070249,0.000006503648,0.00002966313,0.03529455,0.961476,0.0003391658,0.00005089869,0.0002172085],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002283947,0.0001718093,0.9958271,0.00006329237,0.0002488368,0.0005667948,0.00001207117,0.0004723285,0.0003537555],"genre_scores_gemma":[0.4702047,0.00002490715,0.5292849,0.00006262217,0.00001274875,0.0001397986,7.849465e-7,0.00001774341,0.0002518295],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4679207,"threshold_uncertainty_score":0.8021254,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03384658412296238,"score_gpt":0.2831074663206645,"score_spread":0.2492608821977021,"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."}}