{"id":"W4243820936","doi":"10.5194/isprsarchives-xli-b1-765-2016","title":"A ROBUST REGISTRATION ALGORITHM FOR POINT CLOUDS FROM UAV IMAGES FOR CHANGE DETECTION","year":2016,"lang":"en","type":"article","venue":"˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Yarmouk University; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Point cloud; Computer science; Landslide; Computer vision; Orientation (vector space); Artificial intelligence; Iterative closest point; Image registration; Epoch (astronomy); Robustness (evolution); Point set registration; Bundle adjustment; Remote sensing; Matching (statistics); Point (geometry); Image (mathematics); Geology; Mathematics; Geometry","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0007742823,0.001466648,0.001390232,0.002676349,0.000905793,0.001283411,0.001797826,0.001140791,0.00214181],"category_scores_gemma":[0.002470777,0.001024148,0.002002837,0.0034833,0.0005406224,0.001471723,0.001984145,0.001945146,0.002837314],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000651742,"about_ca_system_score_gemma":0.001565537,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004286946,"about_ca_topic_score_gemma":0.004600069,"domain_scores_codex":[0.9984077,0.0001450376,0.000116303,0.0004062409,0.0008004084,0.0001243997],"domain_scores_gemma":[0.9992965,0.0001066636,0.0001201313,0.0001699909,0.000279653,0.00002704978],"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":[0.0001168504,0.00009356227,0.0009470151,0.0001902729,0.0001477984,0.0001820842,0.0001377279,0.09218274,0.06558046,0.007024406,0.008477763,0.8249193],"study_design_scores_gemma":[0.00002560103,0.0000861793,0.001695697,0.00002458235,0.00003682005,0.0002557942,0.00005502424,0.9518716,0.0284388,0.004328545,0.01312178,0.00005957556],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00225843,0.0000898041,0.9954052,0.00002552711,0.00004128577,0.00007696897,0.00008688123,0.001690616,0.000325358],"genre_scores_gemma":[0.03410567,0.0001663068,0.9633419,0.00003188498,0.0000349957,0.0002531907,0.0007444713,0.0003652418,0.0009563131],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004286946,"threshold_uncertainty_score":0.008524001,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02616863120144989,"score_gpt":0.2552623709025533,"score_spread":0.2290937397011034,"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."}}