{"id":"W2047825202","doi":"10.1109/3dpvt.2006.36","title":"Belief Propagation for Panorama Generation","year":2006,"lang":"en","type":"article","venue":"","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; University of Ottawa","funders":"","keywords":"Panorama; Ghosting; Computer vision; Belief propagation; Computer science; Artificial intelligence; Markov random field; Computer graphics (images); Process (computing); Markov process; Image (mathematics); Algorithm; Mathematics; Image segmentation","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":[0.00005669926,0.00003666084,0.00003173204,0.00002712916,0.00007989081,0.00009262623,0.0001028267,0.00001084382,0.000006518821],"category_scores_gemma":[0.000009429866,0.00003005498,0.00001657001,0.00008157216,0.00000506266,0.0004430126,0.0000215556,0.00001426205,0.00002046629],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001206736,"about_ca_system_score_gemma":0.00001125795,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006985656,"about_ca_topic_score_gemma":0.000004403237,"domain_scores_codex":[0.9996243,0.000005911628,0.00008353915,0.0001353952,0.00006808923,0.00008272481],"domain_scores_gemma":[0.9997702,0.00001038222,0.00002453866,0.000121236,0.00006063347,0.00001301727],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[8.661943e-7,0.000031006,0.00008428282,0.000003132708,7.291544e-7,2.902473e-7,0.00002428518,0.0005486592,0.07140636,0.5004651,0.01103925,0.4163961],"study_design_scores_gemma":[0.0001575735,0.00002172563,0.0003023289,0.000001474942,4.923038e-7,0.000001047329,0.000001944842,0.9095516,0.05719152,0.0100244,0.02268819,0.00005767077],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0009979828,0.00001813226,0.9938965,0.001206072,0.0001273662,0.0001318602,2.057471e-7,0.0001158387,0.003506069],"genre_scores_gemma":[0.311018,8.331652e-7,0.6853424,0.0004785078,0.0001564332,0.0000182346,0.000008624073,0.000002954897,0.002974034],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.909003,"threshold_uncertainty_score":0.1225607,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01788100354218998,"score_gpt":0.2684427536445333,"score_spread":0.2505617501023433,"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."}}