{"id":"W2502557613","doi":"","title":"Image Enhancement Based on Edge Profile Acutance","year":2013,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Region of interest; Pixel; Artificial intelligence; Computer vision; Image gradient; Image (mathematics); Measure (data warehouse); Enhanced Data Rates for GSM Evolution; Boundary (topology); Mathematics; Feature (linguistics); Morphological gradient; Edge detection; Image quality; Computer science; Pattern recognition (psychology); Image processing","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":[0.0002392775,0.0006211489,0.0003044464,0.0006771652,0.0002727341,0.0005563754,0.0003748095,0.0004733902,0.003355722],"category_scores_gemma":[0.0006611368,0.0002309225,0.0003483287,0.0005512615,0.0002744178,0.0008196689,0.0004967549,0.0005785374,0.00066504],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001284375,"about_ca_system_score_gemma":0.0002017476,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002778879,"about_ca_topic_score_gemma":0.0004177869,"domain_scores_codex":[0.9998419,0.00002525458,0.00000859717,0.00003040169,0.00007073029,0.00002308771],"domain_scores_gemma":[0.9996382,0.0001042434,0.00003600883,0.00005751562,0.000138328,0.0000257264],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006163544,0.00009495514,0.0008517087,0.000243826,0.00002799149,0.0003636087,0.00008241007,0.002527962,0.734594,0.002967965,0.0008645327,0.2567647],"study_design_scores_gemma":[0.00004517689,0.0004115021,0.004859232,0.00005137763,0.0001157034,0.002631214,0.00005847031,0.1034511,0.876371,0.001050831,0.01091236,0.00004212323],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1546149,0.001656218,0.8302274,0.0001952731,0.0001688698,0.000139153,0.00007512791,0.001173376,0.01174963],"genre_scores_gemma":[0.5113209,0.001813712,0.474545,0.0002169803,0.0001106609,0.00008746705,0.0001405596,0.0002401907,0.0115244],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003355722,"threshold_uncertainty_score":0.011226,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008427641815396915,"score_gpt":0.2430555411579397,"score_spread":0.2346278993425428,"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."}}