{"id":"W2033366399","doi":"10.1109/cvpr.2010.5540213","title":"Dynamic texture recognition based on distributions of spacetime oriented structure","year":2010,"lang":"en","type":"article","venue":"","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":74,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Texture (cosmology); Representation (politics); Computer science; Histogram; Artificial intelligence; Aggregate (composite); Matching (statistics); Image texture; Computer vision; Orientation (vector space); Motion (physics); Pattern recognition (psychology); Texture synthesis; Image (mathematics); Image processing; Mathematics; Geometry","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":[0.0002663976,0.000269783,0.0005073444,0.001726449,0.0001655176,0.0007561348,0.0004025067,0.0002795985,0.001276669],"category_scores_gemma":[0.001949855,0.0001536682,0.0003215147,0.001383071,0.0004417432,0.001042954,0.0003701799,0.000384848,0.0007240439],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003543693,"about_ca_system_score_gemma":0.0002626637,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001583459,"about_ca_topic_score_gemma":0.001458671,"domain_scores_codex":[0.9997361,0.00002857645,0.00001117207,0.0000771969,0.0001007687,0.00004606231],"domain_scores_gemma":[0.9992794,0.0002072532,0.000151323,0.0001173514,0.0001928047,0.00005181246],"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":[0.0005909331,0.00008462922,0.0063875,0.0001294662,0.00004762006,0.0002248358,0.0001330953,0.03209406,0.137207,0.007646438,0.002502869,0.8129514],"study_design_scores_gemma":[0.00003628936,0.0001461416,0.0195153,0.00001980573,0.00004660726,0.001186395,0.0001655046,0.8954833,0.06884025,0.009489321,0.005004237,0.00006687365],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.147564,0.0003195845,0.8479026,0.0001014537,0.00005267578,0.0000479154,0.0003374845,0.001273182,0.002401081],"genre_scores_gemma":[0.8557107,0.0004250689,0.1416851,0.00005042913,0.00006600662,0.00003319826,0.0005476712,0.0001096909,0.001372],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001726449,"threshold_uncertainty_score":0.004270911,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006142846631537463,"score_gpt":0.2379940550832826,"score_spread":0.2318512084517452,"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."}}