{"id":"W2128674615","doi":"10.1109/smbv.2001.988759","title":"Multi-resolution stereo matching using genetic algorithm","year":2002,"lang":"en","type":"article","venue":"","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; University of Tsukuba; Carnegie Mellon University","keywords":"Quadtree; Computer science; Artificial intelligence; Computer vision; Matching (statistics); Pixel; Image resolution; Algorithm; Genetic algorithm; Pattern recognition (psychology); Mathematics; Machine learning","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":[0.0006549934,0.0005204654,0.0008082738,0.001165513,0.0004406999,0.0005778243,0.001058382,0.001171527,0.001216803],"category_scores_gemma":[0.001213603,0.0003634277,0.0007630283,0.0007187924,0.0004339273,0.0009581115,0.0006952395,0.0005996444,0.0003334908],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008784203,"about_ca_system_score_gemma":0.0007767222,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003914593,"about_ca_topic_score_gemma":0.002893828,"domain_scores_codex":[0.999552,0.00008066519,0.00001965057,0.00009205492,0.0002131086,0.00004259775],"domain_scores_gemma":[0.9996978,0.0001090628,0.00004501145,0.00004215482,0.0000877628,0.00001819928],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000933789,0.0001435299,0.001121055,0.00009771792,0.0001135937,0.0001957941,0.0001752405,0.568685,0.03652444,0.01933707,0.001200625,0.3723126],"study_design_scores_gemma":[0.00002034955,0.00003333999,0.0002755549,0.000009392462,0.00001562857,0.00008311,0.00001304141,0.988039,0.005073688,0.004569495,0.001852624,0.00001481397],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01289364,0.0001273247,0.9851201,0.0000576696,0.00002484588,0.00003092766,0.00001358105,0.0004605078,0.001271395],"genre_scores_gemma":[0.2115859,0.0001802899,0.7861006,0.0001029136,0.00002123146,0.0001150126,0.00008495169,0.00007160906,0.001737314],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003914593,"threshold_uncertainty_score":0.007783592,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05057894042988609,"score_gpt":0.2956820546528569,"score_spread":0.2451031142229708,"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."}}