{"id":"W2175526744","doi":"10.1109/newcas.2011.5981217","title":"Hybrid discrete wavelet transform and Gabor filter banks processing for mammogram features extraction","year":2011,"lang":"en","type":"article","venue":"","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Gabor transform; Artificial intelligence; Pattern recognition (psychology); Discrete wavelet transform; Gabor wavelet; Computer science; Gabor filter; Wavelet transform; Wavelet; Kernel (algebra); Filter bank; Computer vision; Standard deviation; Mathematics; Feature extraction; Filter (signal processing); Stationary wavelet transform; Time–frequency analysis; Statistics","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":[0.000644226,0.0004260937,0.0006875687,0.001235531,0.0001500772,0.0006079528,0.0004281652,0.0006131355,0.00137406],"category_scores_gemma":[0.001165079,0.0003421768,0.0005911221,0.001097535,0.0002264216,0.0008187211,0.0003483564,0.0004202614,0.00104707],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002097455,"about_ca_system_score_gemma":0.0002367869,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006131998,"about_ca_topic_score_gemma":0.0009682766,"domain_scores_codex":[0.9994155,0.00007905905,0.00003791631,0.00007211972,0.0003560507,0.00003930831],"domain_scores_gemma":[0.9996452,0.0001438665,0.00003788187,0.00005345262,0.0001049186,0.00001464624],"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":[0.0003064125,0.0001080382,0.00109932,0.0002387461,0.0001137673,0.000265388,0.00006716954,0.01338782,0.2534946,0.00398837,0.002660124,0.7242702],"study_design_scores_gemma":[0.00008752711,0.0004737984,0.009537746,0.00005852127,0.0002013366,0.001973652,0.0000650285,0.7270759,0.2277327,0.005811195,0.02688815,0.0000943429],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01282181,0.0006127461,0.9852255,0.00004903177,0.00006501398,0.00003069069,0.00006947704,0.0006297842,0.0004958881],"genre_scores_gemma":[0.1266468,0.0009223163,0.8691801,0.00008611535,0.0001090485,0.0001238565,0.0002721346,0.00008531996,0.002574407],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00137406,"threshold_uncertainty_score":0.00459671,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01247027994307217,"score_gpt":0.2489323371960047,"score_spread":0.2364620572529325,"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."}}