{"id":"W2166183883","doi":"10.1109/iscas.2008.4542228","title":"A feature-based image registration technique for images of different scale","year":2008,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Affine transformation; Image registration; Computer vision; Feature (linguistics); Pattern recognition (psychology); Computer science; Feature extraction; Wavelet; Robustness (evolution); Zernike polynomials; Mathematics; Image (mathematics); Geometry","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":[0.0008092707,0.0005800698,0.0009314394,0.001271807,0.0003810285,0.0006634776,0.001087002,0.00105284,0.001966828],"category_scores_gemma":[0.002386998,0.00046657,0.001205,0.001505159,0.0006622286,0.001429757,0.0008640774,0.001269481,0.001717617],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002952187,"about_ca_system_score_gemma":0.0004137009,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003646391,"about_ca_topic_score_gemma":0.0005307528,"domain_scores_codex":[0.9991975,0.0001136341,0.00004333596,0.0001814443,0.0004072247,0.00005676963],"domain_scores_gemma":[0.9989306,0.0002446333,0.0001578357,0.0003980884,0.0002361283,0.00003275209],"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":[0.0002996544,0.00007660468,0.0004569834,0.0002682992,0.0001043126,0.0002865575,0.0001892225,0.006748088,0.4572822,0.007876568,0.00195523,0.5244563],"study_design_scores_gemma":[0.00008392805,0.001134834,0.007168154,0.00008059044,0.0003029263,0.007715867,0.0001585138,0.3025251,0.6220117,0.009422315,0.04916014,0.0002359707],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00610701,0.000237404,0.992424,0.0000552942,0.00005450037,0.00004137338,0.00002412045,0.000666859,0.0003893399],"genre_scores_gemma":[0.04960386,0.0002964091,0.9484562,0.00005397006,0.00005303087,0.00006941329,0.00009432116,0.0001616717,0.001211236],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001966828,"threshold_uncertainty_score":0.006579697,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01697505044178111,"score_gpt":0.2833139941594688,"score_spread":0.2663389437176877,"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."}}