{"id":"W2466084814","doi":"10.5194/isprs-archives-xli-b7-195-2016","title":"SPMK AND GRABCUT BASED TARGET EXTRACTION FROM HIGH RESOLUTION REMOTE SENSING IMAGES","year":2016,"lang":"en","type":"article","venue":"˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"University of Waterloo; National Natural Science Foundation of China; National Geographic Society","keywords":"Artificial intelligence; Scale-invariant feature transform; Computer science; Computer vision; Rectangle; Pattern recognition (psychology); Segmentation; Feature extraction; Histogram; Support vector machine; Classifier (UML); Image (mathematics); Mathematics","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":[0.0003154354,0.0008928733,0.0008929924,0.002489679,0.0003765383,0.0008579032,0.0009375191,0.0008147637,0.002953952],"category_scores_gemma":[0.0007224246,0.000515807,0.000832009,0.00124906,0.0003938836,0.001380996,0.0007080538,0.0004890363,0.001604074],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000374597,"about_ca_system_score_gemma":0.0005555726,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002566748,"about_ca_topic_score_gemma":0.004053824,"domain_scores_codex":[0.9993132,0.00004331379,0.00003171729,0.0001752027,0.000357003,0.00007955146],"domain_scores_gemma":[0.9996697,0.00004043909,0.00004427042,0.00005593841,0.0001648467,0.00002496751],"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":[0.0003514034,0.00007542059,0.002760917,0.0003868111,0.0001044492,0.0003988121,0.0001234526,0.01314226,0.3047462,0.001592831,0.004401343,0.6719162],"study_design_scores_gemma":[0.00004922715,0.0002167199,0.02035434,0.00004213678,0.0001100707,0.001706463,0.000198544,0.7465951,0.2171972,0.003336735,0.01009885,0.00009466131],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09077087,0.0007733571,0.8980916,0.0001416668,0.00009034776,0.0001499298,0.0004389244,0.006367547,0.003175748],"genre_scores_gemma":[0.4684902,0.0005758608,0.5228243,0.0001952267,0.0000641983,0.0001446193,0.002225216,0.0005727108,0.004907544],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002953952,"threshold_uncertainty_score":0.009881914,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0160061381245283,"score_gpt":0.2659206772831068,"score_spread":0.2499145391585784,"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."}}