{"id":"W1547211606","doi":"10.1007/978-3-642-03641-5_23","title":"Image Registration under Varying Illumination: Hyper-Demons Algorithm","year":2009,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Health Sciences Centre; University of Toronto; Sunnybrook Health Science Centre","funders":"","keywords":"Generalization; Computer science; Extension (predicate logic); Algorithm; Optical flow; Image registration; Artificial intelligence; Point (geometry); Image (mathematics); Computer vision; Interpretation (philosophy); 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":[0.001038033,0.0007691144,0.000773682,0.001294219,0.0005098401,0.001318112,0.001286576,0.00142325,0.004117511],"category_scores_gemma":[0.00199992,0.0004781347,0.0008208459,0.001666123,0.0006633549,0.001271687,0.001469225,0.001099956,0.002177692],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004593101,"about_ca_system_score_gemma":0.0009618793,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00189488,"about_ca_topic_score_gemma":0.00301964,"domain_scores_codex":[0.9993722,0.0001654444,0.00003209783,0.0001442589,0.0002388015,0.00004733511],"domain_scores_gemma":[0.9995408,0.000122949,0.0000421283,0.0001445793,0.0001219317,0.00002758587],"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":[0.0003966492,0.00006934869,0.0006683391,0.0002288931,0.0001568629,0.0001483317,0.000170103,0.04957411,0.05167226,0.02612674,0.009050625,0.8617378],"study_design_scores_gemma":[0.00006168539,0.000146176,0.001687955,0.00005051698,0.0001088866,0.001060678,0.0001086816,0.8612215,0.07765122,0.02485612,0.03296924,0.00007738396],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005848846,0.0003301818,0.9901631,0.0001483349,0.00006151145,0.00004648551,0.00006497078,0.0008364505,0.00250027],"genre_scores_gemma":[0.06573872,0.0004466163,0.9273697,0.0001109285,0.00004545353,0.00006881616,0.0002856368,0.0003960187,0.005538028],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004117511,"threshold_uncertainty_score":0.01377445,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01867295029330719,"score_gpt":0.2747065265540911,"score_spread":0.2560335762607839,"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."}}