{"id":"W1981212635","doi":"10.1109/iscas.2008.4541986","title":"Graph cut video object segmentation using histogram of oriented gradients","year":2008,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Artificial intelligence; Segmentation; Histogram; Computer science; Computer vision; Cut; Luminance; Image segmentation; Segmentation-based object categorization; Graph; Scale-space segmentation; Pattern recognition (psychology); Image (mathematics); Theoretical computer science","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":[0.0003222045,0.0006608926,0.0007685646,0.002086714,0.0003441323,0.001061471,0.00102189,0.000945624,0.00182268],"category_scores_gemma":[0.001086276,0.0004728558,0.0006923876,0.001170626,0.0004598481,0.001298264,0.0006425226,0.0006547107,0.0007243459],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007738638,"about_ca_system_score_gemma":0.0005713029,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004866822,"about_ca_topic_score_gemma":0.005290071,"domain_scores_codex":[0.9996058,0.00004522511,0.00001963361,0.000100324,0.0001900112,0.00003887013],"domain_scores_gemma":[0.9995647,0.0001255375,0.00004843024,0.00006405001,0.0001627241,0.00003454337],"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":[0.0002801374,0.00009191246,0.0008630314,0.000167921,0.0001032458,0.0002144141,0.0001044559,0.07985897,0.1235386,0.01063253,0.004757025,0.7793878],"study_design_scores_gemma":[0.00002655035,0.00009265224,0.00158253,0.00001906504,0.00002772805,0.0002617256,0.00003239062,0.9209945,0.06153192,0.01000918,0.005377233,0.00004445533],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008590143,0.0001650968,0.9885942,0.000060675,0.00003565192,0.00005106639,0.00007108092,0.00167744,0.0007547113],"genre_scores_gemma":[0.106744,0.0002552468,0.8908655,0.00008982173,0.00004513093,0.00005903526,0.0003985201,0.0003300777,0.001212709],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004866822,"threshold_uncertainty_score":0.009676933,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03158455131903942,"score_gpt":0.2822583373129719,"score_spread":0.2506737859939325,"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."}}