{"id":"W3042695477","doi":"10.23977/acss.2020.040106","title":"Nature Inspired Algorithms multi-objective histogram equalization for Grey image enhancement","year":2020,"lang":"en","type":"article","venue":"Advances in Computer Signals and Systems","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Histogram equalization; Artificial intelligence; Particle swarm optimization; Computer science; Image quality; Computer vision; Histogram; Image processing; Adaptive histogram equalization; Pattern recognition (psychology); Image segmentation; Image (mathematics); Algorithm","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002688433,0.0004612747,0.00045214,0.0004464547,0.0002094105,0.0005078396,0.0005186084,0.0005141385,0.002876013],"category_scores_gemma":[0.0004462968,0.0001774804,0.000535524,0.0003882337,0.0002461455,0.0004729919,0.0003526097,0.0005619017,0.0005497542],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003595489,"about_ca_system_score_gemma":0.0003370299,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001287463,"about_ca_topic_score_gemma":0.001183571,"domain_scores_codex":[0.9998135,0.0000263598,0.000009127044,0.00002586283,0.0001104364,0.00001466361],"domain_scores_gemma":[0.9998806,0.00004426372,0.00002047371,0.000009128806,0.00004117103,0.00000443364],"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":[0.0001057805,0.0001469234,0.001112258,0.0005067533,0.0001476921,0.0001795882,0.0001690548,0.4021349,0.06662109,0.02454572,0.005406916,0.4989234],"study_design_scores_gemma":[0.00001413814,0.00009134987,0.0006851563,0.00003009019,0.00002574207,0.0001324838,0.00002416257,0.9755679,0.009507629,0.004621711,0.009279798,0.00001996636],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009042207,0.001418849,0.9793761,0.0001532755,0.00008212168,0.00007540718,0.00002666889,0.0004759027,0.009349504],"genre_scores_gemma":[0.4713502,0.002356834,0.5021721,0.0002682899,0.00009985882,0.0003156962,0.00015925,0.000155725,0.02312206],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002876013,"threshold_uncertainty_score":0.009621203,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02335341672041348,"score_gpt":0.3076920205203799,"score_spread":0.2843386037999664,"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."}}