{"id":"W2802408754","doi":"10.3390/rs10050682","title":"Infrared Image Enhancement Using Adaptive Histogram Partition and Brightness Correction","year":2018,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":69,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Artificial intelligence; Histogram; Grayscale; Computer science; Histogram matching; Computer vision; Brightness; Smoothing; Image histogram; Adaptive histogram equalization; Maxima and minima; Pattern recognition (psychology); Histogram equalization; Image processing; Pixel; Mathematics; Image (mathematics); Color image; Optics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003438867,0.0005350143,0.0004609613,0.0008853935,0.0002440938,0.0004860707,0.0007076896,0.0003626177,0.001230223],"category_scores_gemma":[0.0008061248,0.0002702812,0.000550498,0.00085096,0.0004082244,0.0008830585,0.0006530474,0.0005039645,0.0004518556],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000254433,"about_ca_system_score_gemma":0.0003103311,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001005135,"about_ca_topic_score_gemma":0.001042594,"domain_scores_codex":[0.9997168,0.00003271416,0.00001366996,0.00007606154,0.0001299842,0.00003071383],"domain_scores_gemma":[0.9996941,0.00008112593,0.00005206757,0.0000574679,0.0001015882,0.00001366356],"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":[0.0002793919,0.0001470915,0.00119512,0.0001885033,0.00005637628,0.000142518,0.0001965916,0.03717055,0.4990373,0.003711884,0.00127654,0.4565982],"study_design_scores_gemma":[0.00004699537,0.0002473483,0.004555493,0.00002202915,0.00009976702,0.0008176789,0.00008183602,0.552357,0.4316944,0.002284083,0.007722272,0.00007115844],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02405653,0.0002898515,0.973751,0.00004271324,0.0000345845,0.00004900162,0.00001637673,0.0007059171,0.001054005],"genre_scores_gemma":[0.3166025,0.0006239026,0.6788663,0.00008502011,0.00005681982,0.00008196353,0.0001181536,0.0001877923,0.003377651],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001230223,"threshold_uncertainty_score":0.004115462,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02023593964807895,"score_gpt":0.2699998635961172,"score_spread":0.2497639239480383,"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."}}