{"id":"W2029949962","doi":"10.1007/s10514-013-9381-9","title":"Object segmentation in cluttered and visually complex environments","year":2013,"lang":"en","type":"article","venue":"Autonomous Robots","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Magna International (Canada); Toronto Metropolitan University","funders":"","keywords":"Segmentation; Computer science; Artificial intelligence; Conditional random field; Initialization; Computer vision; Object (grammar); Segmentation-based object categorization; Cut; Scale-space segmentation; Image segmentation; Pattern recognition (psychology)","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":[0.0005523755,0.0006616256,0.000764173,0.001458383,0.0006091879,0.001843169,0.0007626413,0.001121933,0.001071228],"category_scores_gemma":[0.002444674,0.0008255584,0.0004341661,0.001311129,0.001101228,0.001800633,0.001041581,0.0003438768,0.0004144894],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006715904,"about_ca_system_score_gemma":0.0007315424,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008018594,"about_ca_topic_score_gemma":0.01124808,"domain_scores_codex":[0.9995565,0.00007685513,0.00002010408,0.0001329982,0.0001199114,0.00009363868],"domain_scores_gemma":[0.9990457,0.000545588,0.0001103485,0.00008428048,0.0001273957,0.00008657404],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0009008214,0.0001865954,0.008843222,0.0004072893,0.0001768905,0.001081517,0.0007552782,0.5028338,0.09855864,0.01063717,0.00277107,0.3728476],"study_design_scores_gemma":[0.00001361268,0.00004552205,0.004328979,0.00001539864,0.00002097659,0.0003115223,0.0001726403,0.9670421,0.01403907,0.01282939,0.001162435,0.00001831377],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.230926,0.001010113,0.7644696,0.0002057215,0.00004740655,0.00004082656,0.0001279318,0.00116818,0.002004097],"genre_scores_gemma":[0.8126991,0.0007035675,0.182533,0.00007639072,0.00005462622,0.00002203694,0.0004120354,0.000285781,0.003213476],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008018594,"threshold_uncertainty_score":0.01594383,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01150153994908729,"score_gpt":0.2098393283177285,"score_spread":0.1983377883686412,"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."}}