{"id":"W2147571468","doi":"10.1109/tcsvt.2006.882388","title":"SPIHT-Based Coding of the Shape and Texture of Arbitrarily Shaped Visual Objects","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems for Video Technology","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Set partitioning in hierarchical trees; Artificial intelligence; Computer science; Computer vision; Rate–distortion theory; Wavelet; Coding (social sciences); Texture compression; Pattern recognition (psychology); Image texture; Pixel; Wavelet transform; Mathematics; Discrete wavelet transform; Data compression; 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":[0.0001426576,0.0002846816,0.0003132979,0.000524096,0.0001862913,0.0004766315,0.0004692354,0.0002514707,0.001517464],"category_scores_gemma":[0.0005650823,0.0001384341,0.0003216438,0.000560718,0.0004158312,0.0006197299,0.0004198049,0.0005569304,0.0004191961],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002873095,"about_ca_system_score_gemma":0.0002758274,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005535307,"about_ca_topic_score_gemma":0.001089167,"domain_scores_codex":[0.99988,0.00001004952,0.000005793991,0.000008928631,0.00008240953,0.00001278894],"domain_scores_gemma":[0.9997455,0.00006654825,0.00002722937,0.00005429988,0.00009298038,0.00001341696],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002701329,0.00005009812,0.0003915876,0.0002728512,0.00003386458,0.0003514584,0.0001504868,0.0525462,0.4671312,0.04096303,0.003513895,0.4343252],"study_design_scores_gemma":[0.00003575649,0.0003419822,0.001255418,0.00005652248,0.00005261786,0.001348556,0.00004513066,0.6530738,0.3030981,0.01554136,0.02509223,0.0000586415],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02442362,0.0003296055,0.9719962,0.00005940015,0.00009091911,0.00004045627,0.0001266165,0.0004194512,0.002513727],"genre_scores_gemma":[0.2983839,0.001185422,0.6911188,0.0001024814,0.000158654,0.0001226664,0.0006698731,0.0001299325,0.008128381],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001517464,"threshold_uncertainty_score":0.005076468,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01432316609738875,"score_gpt":0.2535160707978769,"score_spread":0.2391929047004881,"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."}}