{"id":"W2139806954","doi":"10.1109/icip.2002.1040033","title":"Fractal image compression using MNLPC, MIC and H-MPC network library","year":2003,"lang":"en","type":"article","venue":"Proceedings - International Conference on Image Processing","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Fractal compression; Iterated function system; Computer science; Decoding methods; Image compression; Fractal; Data compression; Fractal transform; Artificial neural network; Artificial intelligence; Pattern recognition (psychology); Algorithm; Principal component analysis; Mathematics; Image (mathematics); Image processing","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":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0003892301,0.0002862768,0.0002236773,0.000244311,0.0003306917,0.002744542,0.0007883994,0.0001185861,0.0001102371],"category_scores_gemma":[0.0001329463,0.0002777333,0.00005047631,0.0003504745,0.0001436348,0.00584407,0.0003207935,0.0004429974,0.00001616215],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000496256,"about_ca_system_score_gemma":0.0002011749,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003997976,"about_ca_topic_score_gemma":2.45013e-7,"domain_scores_codex":[0.9980617,0.00003043659,0.0003861725,0.0006531969,0.0005168018,0.0003516992],"domain_scores_gemma":[0.9988435,0.00004534157,0.0003589438,0.0001336892,0.0004807831,0.0001377681],"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":[0.00007871791,0.0002488386,0.003526648,0.0001682927,0.00004599744,0.00002418641,0.003012237,0.00002610277,0.1972811,0.7600421,0.003232968,0.03231288],"study_design_scores_gemma":[0.0009020254,0.0001479266,0.0009759213,0.001271714,0.00002018284,0.0002358841,0.0004791336,0.7147034,0.1359114,0.1387101,0.005630475,0.001011741],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1037904,0.0002927953,0.6789781,0.003486887,0.0003809919,0.0004431343,0.000006158055,0.001168214,0.2114532],"genre_scores_gemma":[0.6671491,0.00004970009,0.331643,0.0008254964,0.00009903987,0.00001779566,0.000004897724,0.00002473464,0.0001861693],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7146773,"threshold_uncertainty_score":0.9999675,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03526596097694376,"score_gpt":0.3055273453197024,"score_spread":0.2702613843427586,"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."}}