{"id":"W2119023565","doi":"10.1109/icpr.2006.734","title":"Joint Image Segmentation and Interpretation Using Iterative Semantic Region Growing on SAR Sea Ice Imagery","year":2006,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Space Agency","keywords":"Segmentation; Computer science; Markov random field; Artificial intelligence; Image segmentation; Synthetic aperture radar; Scale-space segmentation; Interpretation (philosophy); Radar imaging; Computer vision; Segmentation-based object categorization; Pattern recognition (psychology); Semantic interpretation; Radar","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":[0.001346094,0.0006995769,0.0007782992,0.001488726,0.0004381911,0.001114543,0.0008986023,0.0007665882,0.0005927418],"category_scores_gemma":[0.003453228,0.0005038653,0.0009819831,0.001260968,0.001216546,0.001295794,0.000866371,0.000596629,0.0003523996],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005522728,"about_ca_system_score_gemma":0.0007480533,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002019637,"about_ca_topic_score_gemma":0.003016843,"domain_scores_codex":[0.9992142,0.0002662643,0.00004889558,0.0001413605,0.0002653691,0.00006393529],"domain_scores_gemma":[0.998844,0.0005605873,0.0001703538,0.0001469547,0.0002419383,0.00003609027],"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":[0.0005070193,0.00008401747,0.001677891,0.0002354066,0.0001310407,0.0004773472,0.0008559172,0.3051605,0.1665647,0.0195652,0.00136745,0.5033735],"study_design_scores_gemma":[0.00001369107,0.00004400452,0.0009706769,0.00001208444,0.0000284182,0.0002133457,0.00008372982,0.9493418,0.03655909,0.01126363,0.001445145,0.00002449054],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02034215,0.0001240912,0.9782982,0.00008779339,0.000009316944,0.00003906817,0.0000200618,0.0004263046,0.0006530056],"genre_scores_gemma":[0.1818792,0.0001590349,0.8169352,0.00003237032,0.00002514863,0.00005604078,0.0001274852,0.0001799656,0.000605547],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002019637,"threshold_uncertainty_score":0.00711894,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02087618878066384,"score_gpt":0.2844226047498505,"score_spread":0.2635464159691867,"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."}}