{"id":"W2115839263","doi":"10.1109/crv.2005.54","title":"Modeling Prior Shape and Appearance Knowledge in Watershed Segmentat","year":2005,"lang":"en","type":"article","venue":"","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Simon Fraser University","keywords":"Robustness (evolution); Artificial intelligence; Segmentation; Computer science; Watershed; Image segmentation; Histogram; Cluster analysis; Computer vision; Market segmentation; Scale-space segmentation; Pattern recognition (psychology); Segmentation-based object categorization; Image (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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001629443,0.00006624009,0.00007402089,0.00006457925,0.00003878434,0.00007605161,0.0002364222,0.0000283216,0.0000124067],"category_scores_gemma":[0.000003569411,0.00005331384,0.00001435984,0.0001660834,0.00001674827,0.0004335037,0.0001040411,0.00005895344,0.00004527803],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003034528,"about_ca_system_score_gemma":0.00001533927,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009514174,"about_ca_topic_score_gemma":0.00001242484,"domain_scores_codex":[0.9994142,0.00001853796,0.0001398351,0.0002172109,0.00007121576,0.0001389734],"domain_scores_gemma":[0.999756,0.000006394785,0.00001282299,0.000161117,0.00002668509,0.00003693402],"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":[0.000006628215,0.0001012722,0.0002752474,0.00002592114,0.000002995594,0.000001845507,0.001509184,0.00005704047,0.02179832,0.01377867,0.00008837899,0.9623545],"study_design_scores_gemma":[0.0001587607,0.00001153355,0.0002603302,0.00001465549,5.020236e-7,0.000003206072,0.00002762504,0.9512603,0.04713807,0.000333811,0.0007075246,0.00008363576],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08039992,0.0005487158,0.9146785,0.001600531,0.00002024981,0.0001209774,1.23869e-7,0.0002281911,0.002402863],"genre_scores_gemma":[0.9460986,0.00008421935,0.05284556,0.0002072421,0.00001873384,0.000009404065,2.732378e-7,0.000003254244,0.0007327119],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9622709,"threshold_uncertainty_score":0.2174075,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02337250553253939,"score_gpt":0.2738096181579973,"score_spread":0.2504371126254579,"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."}}