{"id":"W2387021316","doi":"","title":"Algorithm of for 2D Maximum Between-Cluster Image Segmentation Based on GA","year":2011,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Advanced Measurement and Detection Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Otsu's method; Computer science; Image segmentation; Segmentation; Artificial intelligence; Region growing; Computation; Genetic algorithm; Scale-space segmentation; Segmentation-based object categorization; Pattern recognition (psychology); Image (mathematics); Image processing; Noise (video); Gray (unit); Algorithm; Computer vision; Machine learning","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":[0.0005075478,0.0009111722,0.0009678537,0.00120377,0.0007931537,0.0009284733,0.001426336,0.001371145,0.003020221],"category_scores_gemma":[0.001069479,0.0004340378,0.0008534892,0.0008265257,0.0005324429,0.0007858063,0.0007694055,0.0007083626,0.0008088231],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008902916,"about_ca_system_score_gemma":0.001576726,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00949823,"about_ca_topic_score_gemma":0.006164062,"domain_scores_codex":[0.9995362,0.00007138921,0.00002521301,0.0001294372,0.0001766496,0.0000611018],"domain_scores_gemma":[0.9997804,0.00006820354,0.00001915421,0.00002077828,0.0001021413,0.000009309165],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001442927,0.00006454535,0.001265328,0.0002102416,0.0001079846,0.0001695936,0.0003210288,0.5342926,0.02086537,0.02086803,0.006347613,0.4153433],"study_design_scores_gemma":[0.00003162762,0.00003045491,0.0003224707,0.0000159552,0.0000176352,0.00007926744,0.00002919111,0.9879503,0.004096447,0.003944946,0.003460773,0.00002096574],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004575255,0.00009224987,0.9923989,0.00007042082,0.00003948491,0.00006867251,0.00002590867,0.0009051115,0.001824078],"genre_scores_gemma":[0.1246594,0.0001751938,0.8707747,0.0001050884,0.00002459611,0.0004553942,0.000191474,0.0002312531,0.003382857],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00949823,"threshold_uncertainty_score":0.01888591,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03294973441273159,"score_gpt":0.2745326204458221,"score_spread":0.2415828860330905,"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."}}