{"id":"W1992564811","doi":"10.1109/icassp.2007.366101","title":"A Color-Based Gray-Level Compensation Algorithm for Fast Change Detection","year":2007,"lang":"en","type":"article","venue":"","topic":"Advanced Measurement and Detection Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Gray (unit); Gray level; Artificial intelligence; Pixel; Computer science; Computer vision; Change detection; Object detection; Color difference; Pattern recognition (psychology); Algorithm","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":[0.0004621518,0.0001065236,0.0000990256,0.0001328296,0.00008015565,0.00001259403,0.00003464067,0.0000716382,0.00002048196],"category_scores_gemma":[0.00002064411,0.0001074808,0.00005197359,0.0002031666,0.00001040016,0.0001188815,0.00000298248,0.00006477256,0.00001050403],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001027185,"about_ca_system_score_gemma":0.000003836984,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007467593,"about_ca_topic_score_gemma":0.0001712285,"domain_scores_codex":[0.9993951,0.00001045364,0.0001570121,0.0001145249,0.0001211366,0.0002017133],"domain_scores_gemma":[0.9996671,0.00008172278,0.00002444951,0.00008564062,0.00008596281,0.00005511087],"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":[0.00002254944,0.00001128909,0.00003168931,0.00002105962,0.000008678368,3.379828e-7,0.00003328979,0.001042382,0.07869085,0.00004720215,0.00001820062,0.9200725],"study_design_scores_gemma":[0.0008276318,0.000104954,0.001994231,0.000007027548,0.00001190294,0.000001576462,0.0000866806,0.4257478,0.5619191,0.0001403493,0.008986218,0.0001724608],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004269859,0.00003366696,0.9927365,0.000008205615,0.0007528978,0.0004647461,0.000006775509,0.0005102999,0.001217113],"genre_scores_gemma":[0.4972504,0.000002641818,0.5021176,0.00008605223,0.0002782365,0.0001128229,0.00000985814,0.00002587817,0.0001165236],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9199,"threshold_uncertainty_score":0.4382938,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.105139840994697,"score_gpt":0.3112798070188636,"score_spread":0.2061399660241666,"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."}}