{"id":"W270047479","doi":"10.1007/978-3-662-04186-4_6","title":"Color Image Segmentation","year":2000,"lang":"en","type":"book-chapter","venue":"Digital signal processing","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":73,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Artificial intelligence; Computer vision; Segmentation; Segmentation-based object categorization; Computer science; Scale-space segmentation; Image segmentation; Minimum spanning tree-based segmentation; Image texture; Feature (linguistics); Range segmentation; Region growing; Pattern recognition (psychology); Human visual system model; 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":[0.0001128787,0.0007409761,0.0004924329,0.001537318,0.0003868839,0.0009661153,0.0007616974,0.0005450685,0.03971127],"category_scores_gemma":[0.0002025574,0.0003659849,0.0004094431,0.001627117,0.0003965861,0.0009061499,0.0005510976,0.0004906527,0.02248243],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004644313,"about_ca_system_score_gemma":0.0003810188,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001354918,"about_ca_topic_score_gemma":0.003020608,"domain_scores_codex":[0.9998699,0.00000565976,0.000004047117,0.00005064755,0.00005395951,0.00001571555],"domain_scores_gemma":[0.9998795,0.00001361123,0.000006040398,0.00003330365,0.00005652244,0.00001097714],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008458833,0.00004530252,0.0001832877,0.0002530727,0.00002257844,0.00008658569,0.00004405997,0.002990432,0.1368927,0.0183919,0.04351862,0.797487],"study_design_scores_gemma":[0.0000206989,0.0001438856,0.003190866,0.0001106422,0.00009178998,0.001780017,0.00009034968,0.06543923,0.2652341,0.02719777,0.6366377,0.00006277821],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008834364,0.005011062,0.7507166,0.0003913096,0.0005976738,0.0001962679,0.0006936807,0.009254733,0.2243043],"genre_scores_gemma":[0.09322377,0.00729547,0.5262585,0.0006867546,0.0003285145,0.0001443733,0.002592932,0.001852756,0.367617],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03971127,"threshold_uncertainty_score":0.1328474,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0169184196547355,"score_gpt":0.2431762828974244,"score_spread":0.2262578632426889,"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."}}