{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000673764,0.0005703755,0.0006901339,0.000698687,0.0003015375,0.0005820451,0.0006141739,0.0008273551,0.001659818],"category_scores_gemma":[0.0009584858,0.000319269,0.0005505935,0.0005979722,0.000369181,0.0007073312,0.0002194787,0.0004618457,0.0005525469],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008423838,"about_ca_system_score_gemma":0.0005357113,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002585755,"about_ca_topic_score_gemma":0.005945836,"domain_scores_codex":[0.9996555,0.00005678075,0.00002085432,0.00009379489,0.0001220295,0.00005103441],"domain_scores_gemma":[0.9995866,0.0001618732,0.00004098712,0.00006224866,0.0001278966,0.0000204403],"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":[0.0007732141,0.0001985472,0.001349003,0.0002324775,0.00015275,0.0001630144,0.0001162892,0.1233933,0.3614725,0.006070943,0.002656541,0.5034214],"study_design_scores_gemma":[0.00002366334,0.000117601,0.00200018,0.00001070595,0.00006022573,0.0001739644,0.0000196654,0.8752312,0.1176766,0.001275738,0.003380059,0.00003043648],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03191775,0.0003683675,0.9655848,0.00006143127,0.00004631181,0.00003325338,0.00005050506,0.0006368484,0.001300737],"genre_scores_gemma":[0.2075037,0.0002597838,0.789604,0.00008289384,0.00003010249,0.00007938215,0.0001292309,0.0001025865,0.002208266],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002585755,"threshold_uncertainty_score":0.00611192,"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."}}