{"id":"W2068287383","doi":"10.1117/12.463106","title":"&lt;title&gt;Extraction of object skeletons in multispectral imagery by the orthogonal regression fitting&lt;/title&gt;","year":2003,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Remote-Sensing Image Classification","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec","funders":"","keywords":"Artificial intelligence; Piecewise; Cluster analysis; Pattern recognition (psychology); Computer science; Topological skeleton; Mathematics; Image segmentation; Graph; Segmentation; Computer vision; Combinatorics; Active shape model","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":[0.0004122802,0.0006486681,0.0006702013,0.001912561,0.0003743516,0.00105989,0.0007589402,0.0006897394,0.03907079],"category_scores_gemma":[0.001177182,0.0003192712,0.000604317,0.001537433,0.000470257,0.001187528,0.0004722452,0.0005980164,0.02379779],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004967144,"about_ca_system_score_gemma":0.0003664585,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002082776,"about_ca_topic_score_gemma":0.003313967,"domain_scores_codex":[0.9997137,0.00002634114,0.00001594316,0.00006253614,0.0001650484,0.00001631981],"domain_scores_gemma":[0.9993683,0.00009153617,0.00005944948,0.0001420578,0.0003010957,0.00003762413],"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":[0.0002943511,0.00009048714,0.0007086427,0.00045353,0.00003458495,0.0005031906,0.00006564348,0.01557761,0.1228148,0.01496495,0.09561253,0.7488797],"study_design_scores_gemma":[0.00006939706,0.0003102935,0.009319927,0.0001259948,0.00005453233,0.00147374,0.00007833134,0.339674,0.2533397,0.01610933,0.3792983,0.0001464709],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02152055,0.001882735,0.9066074,0.001278353,0.002855123,0.0003633338,0.002800525,0.01533089,0.04736109],"genre_scores_gemma":[0.1086505,0.002560067,0.6544642,0.0004323292,0.001237481,0.0003119232,0.01282805,0.005211853,0.2143035],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03907079,"threshold_uncertainty_score":0.1307048,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01024829459341362,"score_gpt":0.234863884996186,"score_spread":0.2246155904027724,"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."}}