{"id":"W1991194759","doi":"10.1142/s0218001401000794","title":"SHAPE TRACKING AND PRODUCTION USING HIDDEN MARKOV MODELS","year":2001,"lang":"en","type":"article","venue":"International Journal of Pattern Recognition and Artificial Intelligence","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Feature (linguistics); Pattern recognition (psychology); Artificial intelligence; Hidden Markov model; Hamming distance; Computer science; Generalization; Boundary (topology); Viterbi algorithm; Image (mathematics); Markov model; Markov chain; Mathematics; Algorithm; Machine learning","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":[],"consensus_categories":[],"category_scores_codex":[0.0004024428,0.00009473692,0.0001109603,0.0002301932,0.00007424913,0.0003057031,0.0002823246,0.00004451264,0.00004646931],"category_scores_gemma":[0.00008128325,0.00008674262,0.00004670069,0.0001339291,0.00006565075,0.001278492,0.00006801081,0.000128199,0.000005459231],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003643736,"about_ca_system_score_gemma":0.00002845005,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001397156,"about_ca_topic_score_gemma":0.000004313871,"domain_scores_codex":[0.9988765,0.00005305767,0.0004546363,0.0001884121,0.0003227027,0.0001047155],"domain_scores_gemma":[0.9988064,0.00004735482,0.0003101532,0.00007242347,0.0006954118,0.00006824137],"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":[0.00002326174,0.0000480957,0.000195033,0.000003934035,0.00001731437,0.00002889401,0.0002993051,0.000006348187,0.00886625,0.0003824621,0.000006304077,0.9901228],"study_design_scores_gemma":[0.0001129574,0.000193684,0.0009132641,0.0004554984,0.00003184839,0.003346614,0.000660944,0.4649631,0.2284818,0.3001763,0.0002670692,0.0003969224],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2325138,0.0001131349,0.7647833,0.001962113,0.0004815866,0.00005378846,0.000002515055,0.00002405249,0.00006572706],"genre_scores_gemma":[0.9829774,0.001025815,0.01539329,0.0002434654,0.000335235,0.000001506393,0.000001949825,0.000005860354,0.00001550363],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9897259,"threshold_uncertainty_score":0.3537261,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1774786694183182,"score_gpt":0.3381245080843055,"score_spread":0.1606458386659873,"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."}}