{"id":"W2152950896","doi":"10.1109/icip.1995.537465","title":"Region-adaptive transform based on a stochastic model","year":2002,"lang":"en","type":"article","venue":"Proceedings - International Conference on Image Processing","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Discrete cosine transform; Markov chain; Computer science; Covariance; Algorithm; Markov process; Separable space; Basis (linear algebra); Boundary (topology); Mathematics; Image (mathematics); Mathematical optimization; Artificial intelligence; Applied mathematics; Mathematical analysis; Statistics; Machine learning; 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":[0.0006395674,0.0003390926,0.0005604452,0.0003712531,0.0001675521,0.0005753384,0.0007980806,0.0006231067,0.001168041],"category_scores_gemma":[0.001950569,0.0003009966,0.0006210083,0.0004902079,0.0006036561,0.001194787,0.0005789975,0.0009200808,0.0004975278],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005177628,"about_ca_system_score_gemma":0.0007053011,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001498641,"about_ca_topic_score_gemma":0.00160242,"domain_scores_codex":[0.9996809,0.00008399262,0.00001120298,0.00007731965,0.0001188232,0.00002777072],"domain_scores_gemma":[0.9995558,0.0002479174,0.00006429374,0.00004364537,0.00006819393,0.00002010441],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009608973,0.00003102842,0.00036633,0.00006987438,0.00003578885,0.00009797858,0.00006360515,0.803885,0.03121352,0.1144655,0.0007154848,0.0489598],"study_design_scores_gemma":[0.000002583895,0.00001475913,0.00005155424,0.000002013625,0.000003627202,0.00003423618,0.000002513832,0.9930218,0.001642981,0.00483752,0.0003812983,0.000005157142],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004554054,0.00003838202,0.9950395,0.00003347311,0.000005481476,0.000006144877,0.00001085354,0.00005575488,0.0002563089],"genre_scores_gemma":[0.4939989,0.0006808356,0.4991907,0.0001436876,0.00008688397,0.0001404618,0.0002456553,0.0001803057,0.005332564],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001498641,"threshold_uncertainty_score":0.003907502,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08784150999535026,"score_gpt":0.3088822915599849,"score_spread":0.2210407815646347,"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."}}