{"id":"W1995744101","doi":"10.1109/icmcs.2012.6320294","title":"Rotation invariant fuzzy shape contexts based on Eigenshapes and fourier transforms for efficient radiological image retrieval","year":2012,"lang":"en","type":"article","venue":"","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Moncton","funders":"Natural Sciences and Engineering Research Council of Canada; New Brunswick Innovation Foundation","keywords":"Artificial intelligence; Histogram; Invariant (physics); Computer vision; Fourier transform; Rotation (mathematics); Pattern recognition (psychology); Computer science; Fast Fourier transform; Image retrieval; Feature extraction; Feature vector; Translation (biology); Mathematics; Algorithm; Image (mathematics); Mathematical analysis","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003440873,0.0003440772,0.0006920947,0.0015932,0.0002948011,0.0006885779,0.000526055,0.0004201783,0.001374175],"category_scores_gemma":[0.001241272,0.0001774997,0.0007061407,0.001419098,0.0004094135,0.001294035,0.0005933422,0.0004777671,0.0006794235],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003874259,"about_ca_system_score_gemma":0.0003879892,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001422901,"about_ca_topic_score_gemma":0.001692514,"domain_scores_codex":[0.999546,0.00007428291,0.00003249013,0.00007884754,0.00022511,0.00004329389],"domain_scores_gemma":[0.9996644,0.00009925358,0.00005318856,0.00006995033,0.00009466011,0.00001860365],"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":[0.0003235571,0.00007936484,0.0009958299,0.0002210058,0.00006016032,0.0001836426,0.000116103,0.02330459,0.1390455,0.02087831,0.002888055,0.811904],"study_design_scores_gemma":[0.00004972072,0.0003755812,0.007229303,0.00005742327,0.0001120168,0.001521244,0.0002007703,0.8877587,0.06616345,0.01978074,0.01660919,0.0001418558],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06866046,0.003036601,0.9253148,0.0001879076,0.0001479743,0.00007804722,0.0001381278,0.0005143479,0.001921765],"genre_scores_gemma":[0.4878477,0.002080601,0.5066043,0.0001324759,0.0002833808,0.00009898331,0.0004137877,0.00008044409,0.002458313],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0015932,"threshold_uncertainty_score":0.004597127,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02418429021596879,"score_gpt":0.2698113878925193,"score_spread":0.2456270976765506,"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."}}