{"id":"W1486238827","doi":"10.1007/978-3-540-85988-8_56","title":"Shape Analysis with Overcomplete Spherical Wavelets","year":2008,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Morphological variations and asymmetry","field":"Mathematics","cited_by":24,"is_retracted":false,"has_abstract":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Ellison Medical Foundation; National Institute of Biomedical Imaging and Bioengineering; National Center for Research Resources; National Institute of Neurological Disorders and Stroke; National Institutes of Health; National Science Foundation","keywords":"Wavelet; Computer science; Shape analysis (program analysis); Artificial intelligence; Multiresolution analysis; Gabor wavelet; Development (topology); Algorithm; Pattern recognition (psychology); Wavelet transform; Mathematics; Discrete wavelet transform; Mathematical analysis; Static analysis","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":[0.0002760071,0.0001299735,0.000268788,0.0001877498,0.0001965773,0.00005035656,0.0004086541,0.00005063631,0.0001698364],"category_scores_gemma":[0.0001750985,0.00008456385,0.00006381349,0.00353879,0.0003178728,0.0001139513,0.000138136,0.0001807516,0.00000918673],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005096314,"about_ca_system_score_gemma":0.00005930084,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003161754,"about_ca_topic_score_gemma":0.00003236994,"domain_scores_codex":[0.9986126,0.00003922197,0.0001966481,0.0004329015,0.0004092668,0.0003093006],"domain_scores_gemma":[0.9989058,0.0005049387,0.00006926306,0.0003689226,0.00007047185,0.00008061238],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001031941,0.001817036,0.5039269,0.00007280629,0.0004256677,0.001527727,0.003345326,0.1477278,0.005098391,0.01673564,0.0004277003,0.3187918],"study_design_scores_gemma":[0.0002933639,0.000157389,0.1542136,0.00001432716,0.00003998795,0.0001028936,5.244212e-7,0.8271252,0.0009502975,0.0167749,0.00005500058,0.0002724993],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3363208,0.000008017723,0.6632976,0.0001897496,0.00003800359,0.00005721795,9.971046e-7,0.00003318749,0.00005445354],"genre_scores_gemma":[0.5475199,9.745951e-7,0.4520977,0.0003363608,0.00003854973,0.000001960288,6.215949e-7,0.000002948158,0.000001065522],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6793973,"threshold_uncertainty_score":0.3448414,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04150501622492193,"score_gpt":0.2790110223033765,"score_spread":0.2375060060784546,"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."}}