{"id":"W2158788806","doi":"10.1109/icme.2006.262404","title":"Novel Progressive Region of Interest Image Coding Based on Matching Pursuits","year":2006,"lang":"en","type":"article","venue":"","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Region of interest; Coding (social sciences); Computer science; Computer vision; Computational complexity theory; Matching (statistics); Artificial intelligence; Image (mathematics); Scalability; Matching pursuit; Transmitter; Image quality; Pattern recognition (psychology); Algorithm; Mathematics; Telecommunications; Statistics; Compressed sensing","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":[],"consensus_categories":[],"category_scores_codex":[0.00003928334,0.0001198412,0.0001334666,0.0001057154,0.00002428708,0.00002932852,0.0001067577,0.0000501508,0.00001323974],"category_scores_gemma":[0.000007915907,0.0001084334,0.00004825147,0.00008803851,0.00002891585,0.00007244383,0.0000200434,0.0001011225,0.000004142164],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002792582,"about_ca_system_score_gemma":0.000005003487,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006199547,"about_ca_topic_score_gemma":0.00001428825,"domain_scores_codex":[0.9994835,0.000007839865,0.0001639388,0.0001168287,0.0000824828,0.0001454067],"domain_scores_gemma":[0.9996514,0.00004401274,0.00004462275,0.0001946386,0.00004482227,0.00002051948],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002841108,0.0001254794,0.0001287065,0.00007113518,0.00001868627,0.000076966,0.00004847606,0.01532377,0.9576289,0.01304696,0.00905,0.004452499],"study_design_scores_gemma":[0.0002863233,0.00006104336,0.0007988678,0.0005398798,0.00001012464,0.00002019383,0.0000259426,0.1303028,0.8662267,0.00132492,0.0002020494,0.0002010771],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3303066,0.0000477078,0.6197302,0.0001011542,0.0001503331,0.0002203305,0.000003539902,0.00124111,0.04819911],"genre_scores_gemma":[0.9798422,0.000001504845,0.01996764,0.00004079329,0.00005566996,0.000006046332,0.00000543811,0.00002739343,0.00005325434],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6495357,"threshold_uncertainty_score":0.4421785,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03508462567344468,"score_gpt":0.2463326763782802,"score_spread":0.2112480507048355,"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."}}