{"id":"W2147393459","doi":"10.1155/asp.2005.1834","title":"Optimized Multichannel Filter Bank with Flat Frequency Response for Texture Segmentation","year":2005,"lang":"en","type":"article","venue":"EURASIP Journal on Advances in Signal Processing","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; University of Waterloo","funders":"","keywords":"Filter bank; Artificial intelligence; Gabor filter; Computer science; Filter (signal processing); Segmentation; Pattern recognition (psychology); Computer vision; Feature (linguistics); Frequency domain; Image texture; Feature extraction; Scale-space segmentation; Image segmentation","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008629687,0.0002005168,0.0002017899,0.0002504132,0.0002899088,0.0003503816,0.000486203,0.00006162481,0.00002181223],"category_scores_gemma":[0.00009675897,0.0001447267,0.00006089713,0.0004328079,0.00005840207,0.003012215,0.00002437149,0.0003665041,0.000006612303],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001856556,"about_ca_system_score_gemma":0.0001408211,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":1.993305e-7,"about_ca_topic_score_gemma":7.094772e-7,"domain_scores_codex":[0.9983726,0.0001570251,0.0004307606,0.0003303213,0.0004010009,0.0003082631],"domain_scores_gemma":[0.9988369,0.0002624128,0.0003808278,0.0001479658,0.0002759486,0.00009595025],"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":[0.001710345,0.0001103413,0.00007324929,0.0000444561,0.000006975819,0.00003247301,0.0008857285,0.004441939,0.02789305,0.0003015179,0.0000547669,0.9644452],"study_design_scores_gemma":[0.006927419,0.002253047,0.0006279412,0.001580092,0.00002703382,0.0006103409,0.0004914336,0.6429225,0.322422,0.009973533,0.01107468,0.001089958],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001946277,0.001949164,0.9932308,0.002151155,0.00006971313,0.0002878195,0.00000205255,0.0001028064,0.0002602164],"genre_scores_gemma":[0.5372626,0.0001289681,0.4616145,0.0006068193,0.0001599891,0.00003594467,0.000001564649,0.00001646507,0.0001731421],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9633552,"threshold_uncertainty_score":0.5901783,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01819848246428502,"score_gpt":0.3050850072436164,"score_spread":0.2868865247793314,"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."}}