{"id":"W2076890299","doi":"10.1088/1742-6596/256/1/012005","title":"A modular CUDA-based framework for scale-space feature detection in video streams","year":2010,"lang":"en","type":"article","venue":"Journal of Physics Conference Series","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; Nvidia","keywords":"Computer science; CUDA; Modular design; STREAMS; Scale (ratio); Feature (linguistics); Scale space; Space (punctuation); Computer graphics (images); Artificial intelligence; Parallel computing; Image (mathematics); Image processing; Computer network; Operating system; Geography; Cartography","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005113888,0.0009314775,0.0007376296,0.001043937,0.0006910709,0.001400726,0.002277366,0.0005452982,0.003317978],"category_scores_gemma":[0.00199255,0.0004725093,0.0006127785,0.001278248,0.0004881928,0.001096152,0.0008422461,0.001244364,0.001240618],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009098775,"about_ca_system_score_gemma":0.001341611,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008310794,"about_ca_topic_score_gemma":0.008886399,"domain_scores_codex":[0.9996386,0.00004315836,0.00002812168,0.00007041133,0.0001792874,0.00004044227],"domain_scores_gemma":[0.9995272,0.00007702694,0.00004076608,0.00008668733,0.0002181881,0.00005020888],"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":[0.0003764271,0.0001656362,0.002046567,0.0003329786,0.0001637226,0.0005075071,0.000344124,0.1899889,0.05420952,0.0362265,0.03956432,0.6760738],"study_design_scores_gemma":[0.00002979653,0.00003033467,0.0003450541,0.00001190752,0.00001249514,0.0000909783,0.00002019629,0.9714784,0.009623153,0.005393067,0.0129364,0.00002823883],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002931565,0.0001009557,0.9885655,0.00006158364,0.00004623266,0.00006221594,0.0001115106,0.007210316,0.0009102249],"genre_scores_gemma":[0.101069,0.0002380015,0.8943658,0.00007522549,0.0000616147,0.0003198369,0.0007337096,0.0007382571,0.002398639],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008310794,"threshold_uncertainty_score":0.01652485,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01369964222903586,"score_gpt":0.2810294990192133,"score_spread":0.2673298567901775,"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."}}