{"id":"W2015790018","doi":"10.1007/s11806-010-0176-2","title":"A new conception of image texture and remote sensing image segmentation based on Markov random field","year":2010,"lang":"en","type":"article","venue":"Geo-spatial Information Science","topic":"Remote Sensing and Land Use","field":"Earth and Planetary Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Markov random field; Artificial intelligence; Image texture; Image segmentation; Computer science; Computer vision; Markov chain; Image (mathematics); Pattern recognition (psychology); Random field; Segmentation; Mathematics; Machine learning; Statistics","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":[0.0007520138,0.0004891868,0.0007428267,0.001534618,0.0003959368,0.00119786,0.0009187934,0.0009261175,0.001350698],"category_scores_gemma":[0.001608176,0.0004196701,0.001015225,0.001473826,0.001829728,0.002935282,0.0006450376,0.00125993,0.0003003774],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001138149,"about_ca_system_score_gemma":0.0006649874,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003405562,"about_ca_topic_score_gemma":0.001992441,"domain_scores_codex":[0.9993079,0.0001272617,0.00003362058,0.0002260914,0.0002427703,0.00006239033],"domain_scores_gemma":[0.9994074,0.0002790329,0.00007899194,0.00007319493,0.0001263645,0.00003496632],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00006261488,0.00003462806,0.001644539,0.0002394918,0.00008499533,0.0002685233,0.000236543,0.1242248,0.01623117,0.7709765,0.002939759,0.08305646],"study_design_scores_gemma":[0.00001768184,0.00007362958,0.001551533,0.0000368814,0.00004389927,0.0005301954,0.00004926078,0.7563619,0.002970695,0.222826,0.01546532,0.00007292238],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003175065,0.0006440605,0.9935834,0.000290762,0.00008194119,0.00001534983,0.00005053178,0.0001226865,0.002036207],"genre_scores_gemma":[0.3847333,0.003507302,0.5999658,0.0006884728,0.0008332006,0.0001898891,0.0003904455,0.0002593784,0.009432266],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003405562,"threshold_uncertainty_score":0.008257926,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005341359923799509,"score_gpt":0.2255503559877978,"score_spread":0.2202089960639983,"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."}}