{"id":"W1994033675","doi":"10.1117/1.3134137","title":"Segmentation of non-natural objects in landscape images using ridgelet transform","year":2009,"lang":"en","type":"article","venue":"Journal of Electronic Imaging","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Computer Research Institute of Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Université de Montréal","keywords":"Artificial intelligence; Pattern recognition (psychology); Computer science; Linear discriminant analysis; Principal component analysis; Image segmentation; Segmentation; Computer vision; Classifier (UML); Kernel (algebra); Gabor transform; Feature extraction; Contextual image classification; Feature vector; Mathematics; Image (mathematics); Time–frequency 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":[0.0004404939,0.0003643887,0.000591381,0.001407602,0.0001410377,0.0006464216,0.0003267546,0.0005187941,0.000819088],"category_scores_gemma":[0.0007253388,0.0002525359,0.0007041989,0.0008550489,0.0003339763,0.0008230528,0.0002372882,0.0003717152,0.0008156947],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002105776,"about_ca_system_score_gemma":0.0002443474,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000582267,"about_ca_topic_score_gemma":0.0009804836,"domain_scores_codex":[0.9997959,0.00003699283,0.00001352297,0.00004860882,0.00007274216,0.00003239444],"domain_scores_gemma":[0.9996245,0.0001646787,0.00004841664,0.00005778335,0.00008424241,0.00002045141],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003861478,0.0001920017,0.003957682,0.0001812392,0.00009575303,0.0003119579,0.0001697939,0.03893292,0.4829988,0.001859665,0.001121035,0.4697928],"study_design_scores_gemma":[0.00001937484,0.0001611319,0.01807128,0.00001506472,0.00005649867,0.0006361522,0.00008950551,0.8245353,0.1518346,0.002095857,0.0024523,0.00003303528],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1895637,0.000310725,0.8072428,0.00009447979,0.00003409836,0.00004429154,0.0001499798,0.001209462,0.001350424],"genre_scores_gemma":[0.4717636,0.0004347582,0.5253201,0.00004733228,0.00003577957,0.00003250771,0.0006259399,0.0001505941,0.001589427],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001407602,"threshold_uncertainty_score":0.002740145,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004385675403479626,"score_gpt":0.2377478117758263,"score_spread":0.2333621363723467,"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."}}