{"id":"W2974785018","doi":"10.5194/isprs-archives-xlii-2-w16-175-2019","title":"SEGMENTATION OF IMAGE PAIRS FOR 3D RECONSTRUCTION","year":2019,"lang":"en","type":"article","venue":"The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Computer vision; Segmentation; Scale-space segmentation; Computer science; Image segmentation; Segmentation-based object categorization; Range segmentation; RGB color model; Grayscale; Pattern recognition (psychology); Mathematics; Image (mathematics)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004779609,0.001080366,0.0007821748,0.002365365,0.0006775499,0.001676436,0.0009378175,0.001020577,0.01714652],"category_scores_gemma":[0.001231508,0.0006627997,0.001226385,0.00177888,0.0005182266,0.001042043,0.00165267,0.0009509488,0.007507425],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005561649,"about_ca_system_score_gemma":0.00090428,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001391402,"about_ca_topic_score_gemma":0.001626633,"domain_scores_codex":[0.9991902,0.00009203792,0.00005270033,0.0002185143,0.0003519528,0.00009467216],"domain_scores_gemma":[0.9995394,0.00005963378,0.00004551129,0.0001512533,0.0001823197,0.00002189018],"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":[0.0004455488,0.0001073604,0.001131558,0.0004236857,0.00009843498,0.0002812897,0.0003279157,0.01566408,0.2549816,0.009785234,0.007659371,0.709094],"study_design_scores_gemma":[0.00009494986,0.0004854314,0.007654805,0.0001578814,0.0001543605,0.001888679,0.0006472464,0.4757698,0.4116489,0.0195996,0.08176211,0.0001361755],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01428512,0.0002539377,0.9780217,0.00008757068,0.00009516882,0.0002144008,0.0003669363,0.003226365,0.003448811],"genre_scores_gemma":[0.07992254,0.000201867,0.9156811,0.00006614009,0.00002568957,0.0001639633,0.001092609,0.0005296561,0.002316432],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01714652,"threshold_uncertainty_score":0.05736083,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01384841686163885,"score_gpt":0.2644603536167291,"score_spread":0.2506119367550902,"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."}}