{"id":"W2096973030","doi":"10.1109/tgrs.2008.2001685","title":"Efficient FFT-Accelerated Approach to Invariant Optical–LIDAR Registration","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Geoscience and Remote Sensing","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Lidar; Computer science; Fast Fourier transform; Artificial intelligence; Computer vision; Image registration; Transformation (genetics); Outlier; Translation (biology); Ranging; Geometric transformation; Remote sensing; Algorithm; Image (mathematics)","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":[0.0005841016,0.0006643058,0.0007467306,0.001078806,0.0004658461,0.0006316066,0.001029879,0.0006392006,0.004184322],"category_scores_gemma":[0.002561568,0.0003483453,0.0005817813,0.001000148,0.0003343324,0.001067218,0.0009078261,0.0007823335,0.001889996],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004013214,"about_ca_system_score_gemma":0.000866205,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002621945,"about_ca_topic_score_gemma":0.003313928,"domain_scores_codex":[0.9993995,0.00009204265,0.0000300737,0.00007455688,0.000363533,0.00004030959],"domain_scores_gemma":[0.9992535,0.0002133475,0.0000719801,0.0001403381,0.0002958787,0.00002500118],"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":[0.0002465516,0.00008688508,0.0004937482,0.0001215392,0.00004238332,0.0001798979,0.0001725395,0.1372581,0.06492475,0.01359915,0.004147624,0.7787268],"study_design_scores_gemma":[0.00002110378,0.00004125841,0.0002901321,0.000005414667,0.000007022138,0.000142064,0.00002571106,0.9776738,0.01386091,0.00310968,0.004807373,0.0000155501],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004007326,0.0000647542,0.9943702,0.00003974668,0.00003071248,0.0000222178,0.00001961541,0.0006120626,0.00083351],"genre_scores_gemma":[0.06050095,0.0001049387,0.9373916,0.00002969826,0.00004461682,0.00006819778,0.0001353585,0.0001496585,0.001575068],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004184322,"threshold_uncertainty_score":0.01399797,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02705738064492768,"score_gpt":0.2175699230470011,"score_spread":0.1905125424020734,"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."}}