{"id":"W2063525506","doi":"10.1109/embc.2012.6346638","title":"Application of scale-space descriptors for the reliable detection of keypoints for image registration in optical mapping studies in whole heart preparations","year":2012,"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 Calgary","funders":"","keywords":"Computer vision; Artificial intelligence; Computer science; Image registration; Process (computing); Scale (ratio); Pattern recognition (psychology); Contrast (vision); Scale space; Set (abstract data type); Image (mathematics); Image processing; Geography; Cartography","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":[0.002061207,0.0004056629,0.0006642273,0.002639316,0.0002996475,0.001155805,0.0006252427,0.0006749941,0.001279768],"category_scores_gemma":[0.004938011,0.0002723773,0.0005641152,0.002305743,0.0009069212,0.001051407,0.0008688237,0.00065057,0.0005617416],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000444266,"about_ca_system_score_gemma":0.0007475131,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009925729,"about_ca_topic_score_gemma":0.0009353484,"domain_scores_codex":[0.9990977,0.000275976,0.00008786518,0.0001470132,0.0003309249,0.0000604659],"domain_scores_gemma":[0.9982157,0.001011435,0.000161003,0.0002719506,0.0002857548,0.00005423116],"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":[0.0004270995,0.0001452047,0.00236474,0.0005765403,0.0001112811,0.0002582615,0.0002782746,0.03545167,0.2192062,0.01604928,0.001953183,0.7231782],"study_design_scores_gemma":[0.00008340455,0.0005518477,0.01579848,0.0000829543,0.0001832642,0.00107568,0.0003164395,0.7898333,0.1657641,0.01744488,0.008727899,0.000137771],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02864085,0.0005426938,0.9697692,0.0000785593,0.00003088055,0.00008186812,0.00006838656,0.0003240112,0.0004635655],"genre_scores_gemma":[0.396522,0.00085877,0.6013681,0.00003605066,0.00004413326,0.0001569254,0.000240207,0.0001420144,0.0006317949],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002639316,"threshold_uncertainty_score":0.0109008,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05994764206989855,"score_gpt":0.3605490322591979,"score_spread":0.3006013901892994,"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."}}