{"id":"W2026825894","doi":"10.1117/12.383011","title":"<title>Using density and spatial cues for clustering image pixels in real time</title>","year":2000,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Pixel; Computer science; Cluster analysis; Artificial intelligence; Computer vision; Object (grammar); Noise (video); Image (mathematics); Object detection; Object based; Tracking (education); Video tracking; Pattern recognition (psychology)","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":[0.0002896266,0.0001302595,0.0001777281,0.0000582363,0.00004467543,0.00009603801,0.0004297165,0.00009167657,0.00003274451],"category_scores_gemma":[0.0000934107,0.0001154053,0.0001507383,0.0001458969,0.00008853053,0.0003173295,0.00009595979,0.00009994136,0.000005723124],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007363979,"about_ca_system_score_gemma":0.00002011255,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006490899,"about_ca_topic_score_gemma":1.391036e-7,"domain_scores_codex":[0.9990848,1.925617e-8,0.0002715894,0.0002258983,0.0002357095,0.0001820055],"domain_scores_gemma":[0.9993861,0.00004675694,0.0001012722,0.00004207262,0.000377851,0.00004597615],"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":[0.00002922228,0.00004895502,0.00003561097,0.0001943929,0.00005400583,1.246344e-7,0.000134433,0.000005920352,0.8200477,0.1658309,0.002279289,0.01133935],"study_design_scores_gemma":[0.0007770424,0.0002090423,0.0009253857,0.000305481,0.00005716411,0.00002643731,0.00007167512,0.7186551,0.2597169,0.005119585,0.01365518,0.0004809582],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.9776138,0.00004583593,0.008082482,0.0006971182,0.0001031284,0.0003396198,0.00001217153,0.0001201384,0.01298572],"genre_scores_gemma":[0.3093255,0.0003441961,0.6875946,0.0001079225,0.0005035464,0.00007600998,0.000006761793,0.0000593315,0.001982088],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7186492,"threshold_uncertainty_score":0.4706093,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01213113190690448,"score_gpt":0.2393328522920646,"score_spread":0.2272017203851601,"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."}}