{"id":"W2067008501","doi":"10.1109/ccece.2012.6334997","title":"Optimal spatial resolution in vision-based inspetion of pipes using Catadioptric sensors","year":2012,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"","keywords":"Catadioptric system; Computer vision; Image resolution; Artificial intelligence; Computer science; Resolution (logic); Focal length; Projection (relational algebra); Perspective (graphical); Optics; Image sensor; Automated X-ray inspection; Image processing; Image (mathematics); Physics; Lens (geology); 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005127738,0.0003555738,0.0004051802,0.0002622401,0.0001255479,0.0004982596,0.0003651337,0.0004483218,0.0003823409],"category_scores_gemma":[0.001272574,0.0004745801,0.0003222236,0.0002855193,0.0006296947,0.0007621657,0.0006615985,0.0003809211,0.0001234125],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004312285,"about_ca_system_score_gemma":0.0004928896,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001020654,"about_ca_topic_score_gemma":0.001063076,"domain_scores_codex":[0.9996494,0.00008457771,0.00001326116,0.00007193319,0.00015147,0.00002939129],"domain_scores_gemma":[0.9996997,0.0001527762,0.00005029068,0.00002251873,0.00006225915,0.00001252441],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003109381,0.00007347049,0.001445322,0.0004441217,0.00004734464,0.0004738995,0.0003601926,0.5275028,0.3426062,0.04603153,0.000843824,0.07986037],"study_design_scores_gemma":[0.00001446053,0.00009750044,0.0007568458,0.00001730207,0.00001102279,0.0002553799,0.00005242137,0.9578201,0.0342018,0.006025923,0.0007204678,0.00002680533],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06298206,0.0006271097,0.9346337,0.00009701084,0.000009670995,0.00002050793,0.00001948446,0.0000936752,0.001516672],"genre_scores_gemma":[0.7170445,0.0009935405,0.2805759,0.00003772484,0.00001813472,0.00004308439,0.00004708227,0.00003976383,0.001200308],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001020654,"threshold_uncertainty_score":0.003128767,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02215268744030457,"score_gpt":0.2517358117487802,"score_spread":0.2295831243084757,"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."}}