{"id":"W7102732586","doi":"10.1007/978-3-032-03708-4_27","title":"Invariant Pattern Recognition with Selectively Denoising and Ridgelet-Fourier Features","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Image and Object Detection Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Pattern recognition (psychology); Invariant (physics); Noise reduction; Noise (video); Pattern matching; Noisy data; k-nearest neighbors algorithm","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":[0.0004068325,0.0004901371,0.0006191918,0.0006992359,0.0001306928,0.0007501285,0.0005639787,0.0005291624,0.002504099],"category_scores_gemma":[0.0009883101,0.0003156625,0.0006414708,0.001023864,0.000363017,0.0009702426,0.0005584916,0.0006732226,0.002410466],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001930081,"about_ca_system_score_gemma":0.0002474392,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003565015,"about_ca_topic_score_gemma":0.000601971,"domain_scores_codex":[0.9997185,0.00003000067,0.00001758821,0.00005535351,0.0001544259,0.00002407277],"domain_scores_gemma":[0.9997,0.00007921099,0.00002741469,0.00009838349,0.00008184265,0.00001322647],"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":[0.0001627734,0.0001005832,0.0004031424,0.000161387,0.00005453463,0.0001050975,0.00004434327,0.01886564,0.221776,0.01987258,0.003928089,0.7345257],"study_design_scores_gemma":[0.00001828532,0.0002035333,0.001744067,0.00002953296,0.00007207088,0.0009027821,0.00002611163,0.7993499,0.1647306,0.01918031,0.01370938,0.00003346625],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007267417,0.0003450666,0.9897817,0.00004412525,0.00005376927,0.00001593897,0.00004347206,0.0004761783,0.001972359],"genre_scores_gemma":[0.1102723,0.001014488,0.8760613,0.0000903367,0.0001201848,0.00005452426,0.0004367464,0.0002842225,0.01166582],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002504099,"threshold_uncertainty_score":0.008377075,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009224690341339417,"score_gpt":0.2193678222598771,"score_spread":0.2101431319185377,"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."}}