{"id":"W2096888883","doi":"10.1109/igarss.1990.688916","title":"Texture Feature Extraction From Texture Spectrum","year":2005,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Texture (cosmology); Feature extraction; Computer science; Image texture; Artificial intelligence; Pattern recognition (psychology); Extraction (chemistry); Texture compression; Texture filtering; Computer vision; Image segmentation; Image (mathematics)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":{"n_in":0,"stratum":"aff_core","weight":5595.2375,"opus":{"tier":"OUT","genre":"empirical","about_ca":false,"confidence":"high","reason":"Computer vision method for texture feature extraction from texture spectrum; an image analysis technique."},"gpt":{"tier":"OUT","genre":"empirical","about_ca":false,"confidence":"high","reason":"The work develops texture feature extraction methods for image analysis, not a study of research."},"grok":{"tier":"OUT","genre":"empirical","about_ca":false,"confidence":"medium","reason":"Computer vision texture feature extraction; method use, not study of research."}},"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001757957,0.0003935645,0.0007147981,0.002288825,0.0002068094,0.0007129809,0.0002938078,0.000368874,0.002634469],"category_scores_gemma":[0.0007765833,0.0002367637,0.0004287838,0.00128257,0.0002059994,0.0005253414,0.0003537307,0.0004859163,0.001265595],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001827795,"about_ca_system_score_gemma":0.0003123117,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001067244,"about_ca_topic_score_gemma":0.001218909,"domain_scores_codex":[0.9998834,0.00001010123,0.000007970847,0.00002164343,0.00005167743,0.00002515278],"domain_scores_gemma":[0.9997277,0.00006965199,0.00002308238,0.00003159754,0.0001238593,0.00002414311],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003553793,0.00005780885,0.001271873,0.0001467035,0.00002565282,0.0001570374,0.0000473081,0.003720907,0.4693189,0.001249982,0.001828015,0.5218204],"study_design_scores_gemma":[0.00009865431,0.0002799334,0.02622772,0.00006790118,0.0002580021,0.002254428,0.0002437611,0.4966265,0.4518662,0.00743405,0.01458111,0.00006168374],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06967353,0.0005151424,0.9248574,0.0001743199,0.00007236946,0.0001000877,0.0004216777,0.001759424,0.002426011],"genre_scores_gemma":[0.531635,0.001387468,0.4607417,0.0001340027,0.0001381369,0.0001627237,0.001248932,0.0004519224,0.004099976],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002634469,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008106464233252602,"score_gpt":0.2766280374000719,"score_spread":0.2685215731668193,"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."}}