{"id":"W4294310878","doi":"10.1007/s00371-022-02662-4","title":"Edge guidance filtering for structure extraction","year":2022,"lang":"en","type":"article","venue":"The Visual Computer","topic":"Infrared Target Detection Methodologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"Hong Kong Branch of Southern Laboratory of Ocean Science and Engineering Guangdong Laboratory","keywords":"Texture filtering; Artificial intelligence; Computer vision; Smoothing; Computer science; Texture compression; Pixel; Kernel (algebra); Image texture; Texture (cosmology); Filter (signal processing); Bidirectional texture function; Enhanced Data Rates for GSM Evolution; Segmentation; Edge detection; Pattern recognition (psychology); Bilateral filter; Projective texture mapping; Image processing; Image segmentation; Image (mathematics); Mathematics","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":[0.0002331065,0.0005717289,0.0005003902,0.00124955,0.0003765901,0.000799661,0.0005689418,0.0007012603,0.004055241],"category_scores_gemma":[0.0008712698,0.0003533793,0.0004631723,0.0009050505,0.0002744379,0.0008676147,0.0005079979,0.0008162922,0.002225399],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002615541,"about_ca_system_score_gemma":0.0005601341,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003170792,"about_ca_topic_score_gemma":0.006470803,"domain_scores_codex":[0.9997892,0.00001941183,0.000009304046,0.00004432054,0.0001098854,0.00002794462],"domain_scores_gemma":[0.9996927,0.00008565604,0.00002835997,0.00005017095,0.0001289647,0.00001425947],"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":[0.0001254636,0.00006943628,0.0004940357,0.0001234431,0.00002627606,0.00006673567,0.00006366825,0.01207232,0.1954906,0.006091088,0.003861263,0.7815156],"study_design_scores_gemma":[0.00001960919,0.0001433752,0.003691077,0.00007037044,0.0000687289,0.0003239765,0.00007598315,0.7546739,0.2108857,0.009306091,0.02070769,0.00003355539],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01356102,0.0005024883,0.9826642,0.00006831244,0.00003617626,0.00002169978,0.00009062132,0.001109185,0.001946247],"genre_scores_gemma":[0.179274,0.001058482,0.8074328,0.0001401502,0.00005939474,0.00005078262,0.0006539138,0.0003832445,0.01094724],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004055241,"threshold_uncertainty_score":0.01356608,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03303835634974511,"score_gpt":0.3067391548860529,"score_spread":0.2737007985363077,"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."}}