{"id":"W2064907489","doi":"10.1109/mwscas.2010.5548757","title":"Image registration using feature points, Zernike moments and an M-estimator","year":2010,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Zernike polynomials; Artificial intelligence; Affine transformation; Feature (linguistics); Pattern recognition (psychology); Outlier; Image registration; Wavelet; Computer vision; Computer science; Feature extraction; Mathematics; Noise (video); Image (mathematics)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001822847,0.0007027036,0.001361813,0.002237277,0.0004336195,0.001029602,0.001114062,0.001295759,0.001370207],"category_scores_gemma":[0.005809386,0.0005592971,0.001426617,0.002091216,0.001036324,0.00198962,0.001172286,0.001064581,0.001350682],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004170829,"about_ca_system_score_gemma":0.0005650847,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001003899,"about_ca_topic_score_gemma":0.001218926,"domain_scores_codex":[0.9982748,0.0004222833,0.0001160776,0.0003401017,0.0007852053,0.00006157468],"domain_scores_gemma":[0.998122,0.0007451433,0.000311838,0.0004305725,0.0003449981,0.00004548994],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002888409,0.00008321625,0.002051975,0.0004788495,0.0002754192,0.0002586268,0.0002051105,0.04505281,0.0919406,0.02377858,0.002166891,0.8334191],"study_design_scores_gemma":[0.00008067892,0.0006877947,0.008473376,0.0001241138,0.0002517585,0.0034511,0.0001861091,0.7801378,0.1229032,0.03262248,0.05071659,0.0003650434],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002035166,0.0003294986,0.9969646,0.00003353992,0.00003637434,0.00001613723,0.000009300191,0.0003004603,0.0002748279],"genre_scores_gemma":[0.07792578,0.0009939807,0.9189696,0.00005180707,0.00016752,0.0000699985,0.00009902108,0.0002034398,0.001518942],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002237277,"threshold_uncertainty_score":0.009640217,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0174037924557265,"score_gpt":0.327163591688068,"score_spread":0.3097597992323415,"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."}}