{"id":"W2167568179","doi":"10.5194/isprsarchives-xxxviii-5-w12-301-2011","title":"INTEGRATION OF TERRESTRIAL LASER SCANNING POINTS AND 2D FLOOR PLANS BASED ON MAXIMUM SEQUENTIAL SIMILARITY","year":2012,"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":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Centres of Excellence","keywords":"Floor plan; Similarity (geometry); Matching (statistics); Transformation (genetics); Line (geometry); Computer science; Plan (archaeology); Invariant (physics); Line segment; Horizontal and vertical; Series (stratigraphy); Artificial intelligence; Algorithm; Pattern recognition (psychology); Mathematics; Computer vision; Image (mathematics); Geometry; Engineering drawing; Engineering; Geology; Statistics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002693856,0.000382916,0.000430986,0.0009271256,0.0009769904,0.0006793812,0.001580882,0.00009957397,0.000004148739],"category_scores_gemma":[0.001040605,0.0002440878,0.0003061103,0.0008534162,0.002115495,0.0008368616,0.0008058176,0.0004376213,0.000001625331],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003895887,"about_ca_system_score_gemma":0.0002157882,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.494716,"about_ca_topic_score_gemma":0.05833455,"domain_scores_codex":[0.9955376,0.0005281922,0.001298146,0.0003762218,0.001750896,0.0005089746],"domain_scores_gemma":[0.9962295,0.001308262,0.001554128,0.0005158923,0.0002227515,0.0001694857],"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":[0.0001884272,0.00003026372,0.0007290687,0.00003160085,0.0000551561,2.274492e-7,0.002948059,0.002799986,0.002254748,0.00002043323,0.00001924957,0.9909228],"study_design_scores_gemma":[0.0008664133,0.0001700871,0.01122588,0.0003542551,0.00003080807,0.00006757833,0.0006563834,0.9614324,0.01875037,0.005275041,0.0009136116,0.0002571623],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02791206,0.00001759848,0.9623291,0.003248113,0.002278309,0.000481579,0.00008609526,0.0000435864,0.003603508],"genre_scores_gemma":[0.9800788,0.00005259258,0.01859846,0.001045727,0.000168562,3.560745e-7,0.00002818175,0.000008679106,0.00001862185],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9906656,"threshold_uncertainty_score":0.995361,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03206682701587123,"score_gpt":0.2866428155542176,"score_spread":0.2545759885383463,"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."}}