{"id":"W2534574868","doi":"10.1109/icics.2005.1688997","title":"A Hierarchical Algorithm for Fast Projective Flow Estimation","year":2006,"lang":"en","type":"article","venue":"","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Zoom; Computer science; Rotation (mathematics); Algorithm; Translation (biology); Projective test; Motion estimation; Sequence (biology); Inter frame; Coordinate system; Optical flow; Artificial intelligence; Transformation (genetics); Computer vision; Frame (networking); Mathematics; Reference frame; Image (mathematics)","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":[0.0007138504,0.0007384504,0.0005853399,0.001162987,0.0006524228,0.0006745395,0.00131197,0.0007259139,0.005323997],"category_scores_gemma":[0.001791533,0.0005275073,0.0005911849,0.001024862,0.0004736093,0.001321386,0.001360919,0.001066983,0.001987497],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006137912,"about_ca_system_score_gemma":0.00130146,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005977978,"about_ca_topic_score_gemma":0.007022235,"domain_scores_codex":[0.999386,0.00009390043,0.00003395397,0.0001342962,0.0002795553,0.00007220912],"domain_scores_gemma":[0.9995157,0.0001154223,0.00004221166,0.0001249377,0.0001704386,0.00003137571],"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":[0.0001037743,0.00005669611,0.0004409364,0.0001067056,0.00004550984,0.00006326899,0.0001067526,0.1023719,0.02976295,0.03327716,0.007644773,0.8260195],"study_design_scores_gemma":[0.00003755044,0.00008223002,0.0005132348,0.00001421469,0.00001931486,0.00009800799,0.00002041909,0.9650915,0.01044862,0.01137941,0.01226443,0.00003112346],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0008987618,0.00004442721,0.998135,0.00001611323,0.00001374005,0.00002428903,0.00002556542,0.0004580242,0.0003841325],"genre_scores_gemma":[0.02733604,0.0000823688,0.9707445,0.00002816526,0.0000297434,0.00009111996,0.0001971213,0.00009849204,0.001392404],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005977978,"threshold_uncertainty_score":0.01781058,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009856305187871164,"score_gpt":0.2830093449390491,"score_spread":0.2731530397511779,"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."}}