{"id":"W1637150239","doi":"10.1109/icme.2005.1521488","title":"Human Posture Recognition with Convex Programming","year":2005,"lang":"en","type":"article","venue":"","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Matching (statistics); Artificial intelligence; Computer science; Pattern recognition (psychology); Computer vision; Cognitive neuroscience of visual object recognition; Scheme (mathematics); Linear programming; Object (grammar); Similarity (geometry); Convex optimization; Regular polygon; Image (mathematics); Mathematics; Algorithm","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":[0.00007725627,0.00008272145,0.00007568505,0.0000452013,0.00009187183,0.000101523,0.0002406006,0.00003324344,0.00002918659],"category_scores_gemma":[0.000007342084,0.00005912346,0.00002006983,0.00021014,0.00002708438,0.0008598812,0.00005649653,0.00009528763,0.00004487666],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001932555,"about_ca_system_score_gemma":0.0000119021,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005780988,"about_ca_topic_score_gemma":0.00001119309,"domain_scores_codex":[0.9993918,0.00001184022,0.00009450602,0.0002097711,0.0001290358,0.0001630823],"domain_scores_gemma":[0.9995926,0.000009747955,0.00003996016,0.0002208292,0.00009027149,0.00004657039],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000002486727,0.00003618987,0.00008928646,0.000003874777,0.000004030784,0.00000725296,0.00008612227,4.263707e-7,0.002150226,0.004592395,0.0005647403,0.992463],"study_design_scores_gemma":[0.0009083926,0.001454812,0.002085727,0.000108745,0.00001412593,0.0001690494,0.00009002244,0.001006777,0.6906514,0.01442817,0.2882654,0.0008173067],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004412894,0.00004691162,0.9855803,0.0008004562,0.00001194531,0.0001801563,2.891818e-7,0.0008104036,0.008156676],"genre_scores_gemma":[0.3605674,0.000004897904,0.6378116,0.0007773149,0.00005880501,0.00001373056,0.000002803192,0.000005923998,0.0007575834],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9916456,"threshold_uncertainty_score":0.2410985,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01991964909093171,"score_gpt":0.2848141537689922,"score_spread":0.2648945046780605,"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."}}