{"id":"W1820297294","doi":"10.1109/icip.2001.958134","title":"Scene break detection and classification using a block-wise difference method","year":2002,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Artificial intelligence; Computer vision; Computer science; Motion compensation; Motion (physics); Block (permutation group theory); Quarter-pixel motion; Motion estimation; Block-matching algorithm; Motion detection; Matching (statistics); Shot (pellet); Pattern recognition (psychology); Mathematics; Video processing; Video tracking","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":[0.0001798262,0.00007441003,0.00009690236,0.0001109351,0.000142729,0.0001764747,0.0001401476,0.00004521587,0.00001702772],"category_scores_gemma":[0.00002847071,0.00006251663,0.00002894548,0.0004076834,0.00001363347,0.0002815814,0.00005840131,0.00004863726,0.000009343828],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002652245,"about_ca_system_score_gemma":0.000005022697,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007714614,"about_ca_topic_score_gemma":0.00005037365,"domain_scores_codex":[0.9992313,0.00008314887,0.0001534902,0.0002819901,0.0001420956,0.0001079912],"domain_scores_gemma":[0.9995268,0.00003814073,0.00006594029,0.0002500696,0.00006547434,0.00005360215],"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":[0.000001568551,0.00006194479,0.002490462,0.000008099651,0.0000186108,0.000001224921,0.0003440092,0.0003191913,0.1905643,0.008363451,0.00003400769,0.7977931],"study_design_scores_gemma":[0.00009004308,0.0000145323,0.0146762,0.000004372748,0.00001309985,0.00001244815,0.00001101472,0.9811141,0.003349996,0.0005123824,0.0001192974,0.00008249853],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03667571,0.00006298499,0.9618182,0.0002726728,0.00004866133,0.00005243406,1.793489e-7,0.00007441446,0.0009947924],"genre_scores_gemma":[0.8640256,0.00003191379,0.1353338,0.00009980127,0.00001749223,0.000002491771,3.685064e-7,0.000003039331,0.0004854897],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9807949,"threshold_uncertainty_score":0.2549354,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05432997596492305,"score_gpt":0.2740026022647557,"score_spread":0.2196726262998326,"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."}}