{"id":"W1530759905","doi":"10.1007/978-3-540-69812-8_25","title":"Robust 3D Watermarking Technique Using Eigendecomposition and Nonnegative Matrix Factorization","year":2008,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Eigendecomposition of a matrix; Matrix decomposition; Digital watermarking; Non-negative matrix factorization; Factorization; Algorithm; Computer vision; Artificial intelligence; Eigenvalues and eigenvectors; 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":[0.0003362807,0.0008712693,0.000754985,0.0008392104,0.0004294037,0.0007159424,0.0006148483,0.0008634799,0.002479301],"category_scores_gemma":[0.0007531256,0.0004404137,0.0009970294,0.0009786544,0.0004358625,0.001391425,0.0008668734,0.0009526981,0.001015124],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002322342,"about_ca_system_score_gemma":0.0004203836,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004815572,"about_ca_topic_score_gemma":0.0009769622,"domain_scores_codex":[0.9994484,0.00006984449,0.00002940576,0.0001065481,0.0003056815,0.00004007674],"domain_scores_gemma":[0.99958,0.00009273812,0.00006407788,0.0001118091,0.0001372976,0.00001416192],"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":[0.0002254844,0.00009726444,0.0003738508,0.0001946995,0.00007706331,0.0002196079,0.00009889191,0.03442049,0.4640053,0.01713222,0.003874398,0.4792808],"study_design_scores_gemma":[0.00002488208,0.0001675183,0.0009644838,0.00002792085,0.00006088758,0.0009928688,0.00005855401,0.7857349,0.1946913,0.006388274,0.01080374,0.00008456605],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008692072,0.0003157662,0.9890392,0.00008445737,0.00008401833,0.00002173103,0.00004563038,0.0003635154,0.001353657],"genre_scores_gemma":[0.1004952,0.000636233,0.8934579,0.00008831598,0.00007007,0.00006215335,0.0002307722,0.0001174406,0.004841883],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002479301,"threshold_uncertainty_score":0.008294106,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02570028001904287,"score_gpt":0.2578772476415412,"score_spread":0.2321769676224984,"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."}}