{"id":"W2005143499","doi":"10.1007/s11042-006-0072-9","title":"An image rectification scheme and its applications in RST invariant digital image watermarking","year":2006,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Scaling; Template matching; Translation (biology); Image translation; Rectification; Algorithm; Rotation (mathematics); Frequency domain; Digital watermarking; Artificial intelligence; Robustness (evolution); Computer vision; Invariant (physics); Phase correlation; Image (mathematics); Fourier transform; Pattern recognition (psychology); Mathematics; Fourier analysis; Fractional Fourier transform; 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":[0.0003525077,0.0004550243,0.0005364275,0.0007650651,0.0003681344,0.0004532041,0.0004882423,0.0009793782,0.004569624],"category_scores_gemma":[0.001021838,0.000215055,0.000414956,0.0007535345,0.0005205222,0.001033166,0.0003871191,0.0007347542,0.001004077],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001770251,"about_ca_system_score_gemma":0.000172367,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002782747,"about_ca_topic_score_gemma":0.0003570887,"domain_scores_codex":[0.999854,0.00002711938,0.00001075985,0.00003143121,0.00005975338,0.00001699242],"domain_scores_gemma":[0.9996356,0.00009913435,0.00005880752,0.0001338889,0.00005919865,0.00001342907],"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":[0.0004616137,0.00009271202,0.0003402418,0.0002539201,0.00002300384,0.0004149663,0.000194629,0.01065561,0.3613265,0.03198962,0.002103816,0.5921434],"study_design_scores_gemma":[0.0001323415,0.0008913356,0.002823058,0.00008862986,0.0001245194,0.004877409,0.0001250541,0.4081742,0.5201067,0.0176438,0.04489627,0.0001167088],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07647187,0.001878647,0.9125763,0.0005604,0.0002554698,0.0001370065,0.00007262021,0.001341456,0.006706121],"genre_scores_gemma":[0.4059269,0.002854361,0.5695865,0.0001446155,0.0002919948,0.0001064446,0.0001631373,0.0001698079,0.0207563],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004569624,"threshold_uncertainty_score":0.01528692,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01335350937641231,"score_gpt":0.2533002569110888,"score_spread":0.2399467475346765,"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."}}