{"id":"W2295761292","doi":"10.1109/icip.2015.7351240","title":"Object recognition based on deformable edge set","year":2015,"lang":"en","type":"article","venue":"","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Object (grammar); Computer vision; Enhanced Data Rates for GSM Evolution; Computer science; Subspace topology; Pixel; Cognitive neuroscience of visual object recognition; Feature (linguistics); Pattern recognition (psychology); Set (abstract data type); Bounding overwatch; Edge detection; Image (mathematics); Image processing","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":[0.0005694426,0.0007510082,0.001681178,0.002592515,0.0003682309,0.00088912,0.002055852,0.001320941,0.001633304],"category_scores_gemma":[0.001290319,0.0003848427,0.001075841,0.001841357,0.0006666804,0.001847826,0.001211159,0.001083793,0.0009455744],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005372303,"about_ca_system_score_gemma":0.000365654,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002426883,"about_ca_topic_score_gemma":0.002249091,"domain_scores_codex":[0.9993296,0.00005833775,0.00003587243,0.0002091472,0.0002814476,0.00008561888],"domain_scores_gemma":[0.9993727,0.000151887,0.00009414344,0.000177781,0.0001587605,0.00004479746],"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":[0.0002371002,0.0001606725,0.001598257,0.00009031764,0.00009500704,0.0002686943,0.00007971538,0.1125509,0.07935514,0.00768165,0.003146327,0.7947363],"study_design_scores_gemma":[0.000007382354,0.00005212403,0.0009151809,0.000008010137,0.00001427466,0.0002287429,0.00001610914,0.9663331,0.02608213,0.004801002,0.001516975,0.00002494352],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01796438,0.0001851331,0.9796959,0.00005812393,0.00003741413,0.00004199537,0.00007257907,0.001139948,0.000804543],"genre_scores_gemma":[0.3381136,0.0004260603,0.6566725,0.000282554,0.00008570244,0.0001315143,0.000734589,0.0002232676,0.003330202],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002592515,"threshold_uncertainty_score":0.005463958,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07583979592434242,"score_gpt":0.3091209838684773,"score_spread":0.2332811879441348,"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."}}