{"id":"W2989744384","doi":"10.5194/isprs-archives-xlii-2-w17-315-2019","title":"SEAMLESS CO-REGISTRATION OF IMAGES FROM MULTI-SENSOR MULTISPECTRAL CAMERAS","year":2019,"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":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Multispectral image; Artificial intelligence; Computer vision; Computer science; Image registration; Calibration; Pixel; Camera resectioning; Payload (computing); Remote sensing; Image (mathematics); Geography; 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":[0.0006292375,0.0007339686,0.0005358487,0.001390792,0.0003029266,0.0008789356,0.0007272938,0.0007468378,0.001631824],"category_scores_gemma":[0.001291008,0.0004634228,0.0007218454,0.001233093,0.0004184631,0.001487367,0.001649703,0.0009546731,0.001030911],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002861941,"about_ca_system_score_gemma":0.0005443915,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006790445,"about_ca_topic_score_gemma":0.001323593,"domain_scores_codex":[0.9987285,0.0001598095,0.00005631305,0.0004002118,0.0005394575,0.000115779],"domain_scores_gemma":[0.9990201,0.00007324784,0.0001695081,0.0003967104,0.0002823693,0.00005811592],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005257323,0.0003266261,0.007665251,0.0004023437,0.0002645109,0.0004402379,0.0005696038,0.03254803,0.5065507,0.003300023,0.00487407,0.4425329],"study_design_scores_gemma":[0.00005587929,0.0005605762,0.03656586,0.00008115089,0.000153062,0.001935449,0.0005809959,0.4621844,0.4696014,0.003423738,0.02468656,0.0001709513],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1599755,0.0005083185,0.8312061,0.0001494048,0.0002179142,0.000152829,0.0002348374,0.002784608,0.004770393],"genre_scores_gemma":[0.5352927,0.0002481315,0.4612984,0.0001519646,0.00004758622,0.00008555502,0.0005725356,0.0002299162,0.002073212],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001631824,"threshold_uncertainty_score":0.005459011,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01894692147955532,"score_gpt":0.2837384339927435,"score_spread":0.2647915125131882,"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."}}