{"id":"W1888553712","doi":"10.1109/iscas.2002.1010791","title":"On the use of hash functions for defect detection in textures for in-camera web inspection systems","year":2003,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; University of Windsor","funders":"","keywords":"Hash function; Computer science; SHA-2; Cryptographic hash function; Cryptography; Feature hashing; Computer vision; Pattern recognition (psychology); Artificial intelligence; Theoretical computer science; Double hashing; Algorithm; Computer security","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":[0.0004881634,0.0001222775,0.0001909798,0.0003195797,0.00008915177,0.00004836013,0.00003239778,0.0001666825,0.000008358222],"category_scores_gemma":[0.000397624,0.00008954659,0.0001242383,0.0004111557,0.00001107391,0.0001187156,0.000002499068,0.0001464822,0.000005666867],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001562187,"about_ca_system_score_gemma":0.00001610564,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000295839,"about_ca_topic_score_gemma":0.001447279,"domain_scores_codex":[0.9991358,0.00008234778,0.0003649976,0.0001495122,0.0000976881,0.0001696673],"domain_scores_gemma":[0.999043,0.0006558868,0.00005617251,0.0001623673,0.00006205522,0.00002051783],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005287673,0.0001680313,0.0009698367,0.0003633682,0.0001342027,7.045343e-7,0.0002851542,0.8636509,0.08929697,0.02011955,0.01396795,0.01051457],"study_design_scores_gemma":[0.006312125,0.001789323,0.002114689,0.0004904277,0.00007668661,0.00003208673,0.001926548,0.6941255,0.123487,0.0007128564,0.168154,0.0007787152],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8444721,0.00007849828,0.1487569,0.00001202354,0.002766634,0.002378186,0.00002268945,0.0001884916,0.00132457],"genre_scores_gemma":[0.9990088,0.000005020965,0.00004888547,0.00001083313,0.00008999311,0.000578051,0.00000205471,0.00002512297,0.0002312611],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1695253,"threshold_uncertainty_score":0.3651604,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05762708141569638,"score_gpt":0.2387804373876951,"score_spread":0.1811533559719987,"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."}}