{"id":"W2127786001","doi":"10.1109/tip.2009.2024578","title":"$n$-SIFT: $n$-Dimensional Scale Invariant Feature Transform","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":180,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University; University of British Columbia","funders":"","keywords":"Scale-invariant feature transform; Artificial intelligence; Curse of dimensionality; Pattern recognition (psychology); Histogram; Feature extraction; Computer science; Invariant (physics); Matching (statistics); Salient; Computer vision; Feature (linguistics); Image registration; Mathematics; 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":[0.0004428097,0.0005111258,0.0006063311,0.001142527,0.0002718784,0.0006001047,0.0009462603,0.0005017003,0.003142482],"category_scores_gemma":[0.0010737,0.0001954823,0.0005659445,0.001275925,0.0005002491,0.001244799,0.000548127,0.0003584789,0.002007275],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000381142,"about_ca_system_score_gemma":0.0005792489,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00136419,"about_ca_topic_score_gemma":0.002042901,"domain_scores_codex":[0.9996191,0.0000409747,0.00002040852,0.00005926139,0.0002240158,0.00003618806],"domain_scores_gemma":[0.9996781,0.00005846243,0.00005223588,0.00007145371,0.0001195997,0.00002016317],"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":[0.000295301,0.0001420907,0.003126408,0.0002449632,0.000112132,0.0003060671,0.00006173777,0.01914174,0.1000797,0.01402622,0.0183039,0.8441598],"study_design_scores_gemma":[0.0001035127,0.00076834,0.01318732,0.00005381618,0.0001224766,0.002772445,0.00009970494,0.7636994,0.1414563,0.01759383,0.0599431,0.000199853],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01631295,0.0006111962,0.9766699,0.0001463852,0.0001371509,0.0001002615,0.0002738934,0.00224391,0.003504448],"genre_scores_gemma":[0.2557997,0.0007893108,0.7350942,0.0001863885,0.0001394418,0.000186235,0.001500036,0.000233821,0.006070805],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003142482,"threshold_uncertainty_score":0.01051265,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01169971106401026,"score_gpt":0.2738951675905187,"score_spread":0.2621954565265084,"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."}}