{"id":"W4233727872","doi":"10.32920/ryerson.14657649","title":"Wavelet-based image compression using mathematical morphology and self organization feature map","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Artificial intelligence; Image compression; Wavelet transform; Wavelet; Pattern recognition (psychology); Computer vision; JPEG; Computer science; Data compression; Feature (linguistics); Discrete wavelet transform; JPEG 2000; Mathematics; Image processing; 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":[0.0002156897,0.0001893731,0.0002788611,0.0006804622,0.000162088,0.0005310038,0.0002704101,0.0002562553,0.001064592],"category_scores_gemma":[0.0007184956,0.000119474,0.0003398991,0.0007692087,0.0002925135,0.0006658765,0.0002761284,0.0002959286,0.0003751674],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002242897,"about_ca_system_score_gemma":0.000210382,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004221347,"about_ca_topic_score_gemma":0.0004103622,"domain_scores_codex":[0.9998641,0.00001529431,0.000008437522,0.00001633619,0.00008768913,0.000008050679],"domain_scores_gemma":[0.9997949,0.00006796416,0.00002983001,0.00003158506,0.00006894483,0.000006845237],"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":[0.0001337114,0.00007577024,0.0009498612,0.0002070518,0.00005557105,0.0003508466,0.0001601797,0.06448814,0.271616,0.03013813,0.00211017,0.6297146],"study_design_scores_gemma":[0.00002368043,0.0002449403,0.003462407,0.00002783279,0.00003606938,0.0009028403,0.00005779811,0.8521717,0.1200326,0.01201109,0.01099519,0.00003385646],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05324275,0.0006397525,0.941468,0.0001687285,0.0000629126,0.0000717194,0.00006258454,0.0007712055,0.003512296],"genre_scores_gemma":[0.4018387,0.001300352,0.5921952,0.00005509126,0.00006537995,0.00009015423,0.0001731046,0.00009610139,0.004186033],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001064592,"threshold_uncertainty_score":0.003561437,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01531471969667496,"score_gpt":0.2586487544918905,"score_spread":0.2433340347952155,"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."}}