{"id":"W2135424180","doi":"10.1109/crv.2006.68","title":"Sparse Disparity Map from Uncalibrated Infrared Stereo Images","year":2006,"lang":"en","type":"article","venue":"","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Artificial intelligence; Computer vision; Robustness (evolution); Epipolar geometry; Computer science; Phase congruency; Feature (linguistics); Pattern recognition (psychology); Stereo imaging; Wavelet; Computer stereo vision; Stereo cameras; Feature extraction; Matching (statistics); Stereopsis; 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":[0.0001702083,0.0003479882,0.0003947722,0.001164356,0.0001771234,0.0004956812,0.0004924339,0.0003301899,0.002460357],"category_scores_gemma":[0.00118543,0.000306786,0.0003104906,0.001013174,0.0002033754,0.0006092387,0.0008726394,0.0004623825,0.0006251009],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002305832,"about_ca_system_score_gemma":0.0004687886,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001067199,"about_ca_topic_score_gemma":0.001351125,"domain_scores_codex":[0.9997755,0.00001532574,0.000005263802,0.00002291027,0.000159077,0.00002194391],"domain_scores_gemma":[0.9997758,0.00004556575,0.00003316145,0.0000488795,0.00007922933,0.00001730022],"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":[0.0005712524,0.00008865129,0.001566439,0.0002504342,0.00005272817,0.0003488922,0.00020203,0.07915719,0.2975272,0.01163836,0.004212842,0.604384],"study_design_scores_gemma":[0.00005937256,0.0002005277,0.007066815,0.00004066083,0.00003136087,0.0006977903,0.0001571791,0.8310981,0.1403191,0.01207603,0.008198454,0.00005468811],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09554763,0.0001590436,0.8985187,0.0001138167,0.00006030941,0.00007272984,0.0004711031,0.0008442632,0.004212442],"genre_scores_gemma":[0.5327996,0.0003318942,0.4624391,0.00007328271,0.00004753305,0.00007858444,0.001324168,0.0001439515,0.002762026],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002460357,"threshold_uncertainty_score":0.008230746,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01118374010047346,"score_gpt":0.2406813260188441,"score_spread":0.2294975859183707,"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."}}