{"id":"W1493135336","doi":"10.1007/978-3-540-92957-4_49","title":"Automatic Segmentation of Non-rigid Objects in Image Sequences Using Spatiotemporal Information","year":2009,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Canadian Patient Safety Institute","keywords":"Segmentation; Artificial intelligence; Scale-space segmentation; Computer science; Computer vision; Segmentation-based object categorization; Image segmentation; Minimum spanning tree-based segmentation; Pattern recognition (psychology); Spatial analysis; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003356448,0.000904804,0.0009299116,0.002534841,0.0004243849,0.0009055277,0.0008247654,0.0007706019,0.001213387],"category_scores_gemma":[0.0008768038,0.0006810922,0.0009156635,0.002391871,0.0004735949,0.0009901985,0.0006127032,0.0004689998,0.0009221736],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00038562,"about_ca_system_score_gemma":0.0007843142,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004927614,"about_ca_topic_score_gemma":0.008944487,"domain_scores_codex":[0.9997308,0.00002372739,0.00002435121,0.00008320059,0.00008795465,0.00005004136],"domain_scores_gemma":[0.9996241,0.0001013646,0.00007781457,0.00005797038,0.0001050733,0.000033599],"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":[0.0004664282,0.00009680256,0.001513933,0.0004239753,0.0001125616,0.0004612351,0.0002060545,0.02087667,0.4648719,0.00251468,0.002210731,0.5062449],"study_design_scores_gemma":[0.0000308652,0.0002598225,0.01471032,0.0000951793,0.0002512241,0.001760187,0.0002035185,0.7375172,0.2280457,0.007305362,0.009755385,0.00006524524],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07289778,0.002042308,0.9209337,0.0001011296,0.00009559586,0.0001270368,0.0004255976,0.001694907,0.001681914],"genre_scores_gemma":[0.2298406,0.002542245,0.762114,0.00008345131,0.0001010095,0.0001060678,0.001746626,0.0003526483,0.003113302],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004927614,"threshold_uncertainty_score":0.009797871,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01604958594055905,"score_gpt":0.2927901648292021,"score_spread":0.2767405788886431,"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."}}