{"id":"W2162206092","doi":"10.1109/imtc.2005.1604417","title":"Pattern Analysis Using Zernike Moments","year":2006,"lang":"en","type":"article","venue":"2005 IEEE Instrumentationand Measurement Technology Conference Proceedings","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Zernike polynomials; Velocity Moments; Orthogonality; Artificial intelligence; Pattern recognition (psychology); Representation (politics); Feature (linguistics); Computer science; Mathematics; Feature extraction; Computer vision; Optics; Wavefront; Geometry; Physics","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":[0.0007203899,0.0005989633,0.000892173,0.005522619,0.0003661209,0.001876227,0.0005779438,0.0005767844,0.007070223],"category_scores_gemma":[0.003476813,0.0002517128,0.0006661091,0.004614215,0.0004851292,0.001999582,0.0005996267,0.0005783086,0.00259657],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003619443,"about_ca_system_score_gemma":0.0003970036,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001292476,"about_ca_topic_score_gemma":0.0009556459,"domain_scores_codex":[0.9991431,0.00009816471,0.00006109945,0.0001491299,0.0004774633,0.00007092083],"domain_scores_gemma":[0.9987679,0.000445728,0.0001885334,0.0002189713,0.0003432794,0.00003557909],"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":[0.0002193861,0.00008724919,0.00325268,0.0005771796,0.0001370787,0.000260927,0.0002144674,0.02691411,0.1309922,0.01507046,0.006234625,0.8160396],"study_design_scores_gemma":[0.00008782738,0.0005021245,0.03261179,0.0001515625,0.0001808045,0.002251847,0.000601216,0.7299063,0.1395997,0.04544839,0.04841351,0.0002449901],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03452516,0.0008227865,0.9554451,0.0001741004,0.00008070489,0.0001623788,0.0008557719,0.002615451,0.005318613],"genre_scores_gemma":[0.3907174,0.001711212,0.6008645,0.0001044906,0.0001322462,0.0002036701,0.001826268,0.0003233826,0.004117006],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007070223,"threshold_uncertainty_score":0.0236522,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04234839215451858,"score_gpt":0.2635788103095486,"score_spread":0.22123041815503,"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."}}