{"id":"W2172080001","doi":"10.1109/icip.2002.1038993","title":"A predictive contour inertia snake model for general video tracking","year":2003,"lang":"en","type":"article","venue":"Proceedings - International Conference on Image Processing","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer vision; Artificial intelligence; Robustness (evolution); Computer science; Video tracking; Affine transformation; Smoothing; Inertia; Tracking (education); Active contour model; Object (grammar); Mathematics; Image segmentation; Image (mathematics)","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0003705181,0.0002983134,0.0002396494,0.0002520165,0.0003109295,0.001330334,0.0009275672,0.00007021832,0.000028657],"category_scores_gemma":[0.0006696178,0.0002828263,0.00009606359,0.0002391024,0.00008457717,0.003647947,0.000121178,0.0002818447,0.00001088343],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001410235,"about_ca_system_score_gemma":0.0002479184,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001819832,"about_ca_topic_score_gemma":6.891431e-7,"domain_scores_codex":[0.9978084,0.0000100513,0.0004233744,0.0007733322,0.0005340568,0.0004507957],"domain_scores_gemma":[0.9975052,0.0000382345,0.0003223268,0.0001272176,0.001859947,0.0001470369],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001497893,0.0002380688,0.000405559,0.0001220477,0.00005318033,0.000005714006,0.004159476,0.0005159481,0.1023157,0.7324389,0.001057571,0.158538],"study_design_scores_gemma":[0.0007908677,0.00006445043,0.00006438066,0.0002195184,0.000008372413,0.00002259134,0.0002374442,0.9204658,0.01583319,0.06127791,0.0007051544,0.0003102766],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002564737,0.00005979772,0.9597546,0.00157921,0.0002572804,0.0003062226,0.000009587745,0.0002615351,0.03520707],"genre_scores_gemma":[0.6502129,0.00001396092,0.3478411,0.0008463029,0.00009679844,0.000109871,0.000004179271,0.0000228507,0.0008519777],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9199499,"threshold_uncertainty_score":0.9999624,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05475285966709696,"score_gpt":0.3382117595901601,"score_spread":0.2834588999230632,"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."}}