{"id":"W4226106335","doi":"10.1109/jstars.2022.3167264","title":"Global Color Consistency Correction for Large-Scale Images in 3-D Reconstruction","year":2022,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China; Ministry of Natural Resources","keywords":"Artificial intelligence; Color correction; Computer vision; Computer science; Matching (statistics); Homography; Scale (ratio); Color difference; Color image; Pattern recognition (psychology); Mathematics; Image (mathematics); Image processing; Statistics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004255137,0.0000819105,0.0001803352,0.0001918349,0.0002750435,0.00005883453,0.00009812061,0.00003483714,0.000001468293],"category_scores_gemma":[0.00007746511,0.0000855522,0.00003163178,0.0009495223,0.00002213383,0.0002126315,0.00003848544,0.0002612043,7.669871e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001360311,"about_ca_system_score_gemma":0.000164457,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000178274,"about_ca_topic_score_gemma":0.0002087631,"domain_scores_codex":[0.9990057,0.00005275844,0.0004222126,0.0001655583,0.0001738436,0.0001799213],"domain_scores_gemma":[0.999294,0.00007874476,0.0002457187,0.00009530073,0.000248124,0.00003815487],"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":[0.00005565969,0.00004808359,0.001669335,0.000014021,0.000009340674,0.00001235428,0.0004869052,0.009018351,0.01052705,0.001026252,0.0002095479,0.9769231],"study_design_scores_gemma":[0.001373093,0.000105923,0.0260809,0.00006595885,0.000007457231,0.0005763033,0.0006648853,0.9578035,0.001534004,0.007532438,0.004105076,0.0001504462],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2583673,0.00008486803,0.7391449,0.0007288443,0.001215615,0.0001727764,0.000003788386,0.00001857866,0.0002632887],"genre_scores_gemma":[0.3562815,0.00007010544,0.643204,0.000266875,0.0001035673,3.762756e-7,0.000001905389,0.000005671036,0.0000660892],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9767727,"threshold_uncertainty_score":0.3488717,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01708388882784295,"score_gpt":0.2546204218242139,"score_spread":0.237536532996371,"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."}}