{"id":"W2128495139","doi":"10.1109/cvpr.1998.698616","title":"Stereo and color analysis for dynamic obstacle avoidance","year":2002,"lang":"en","type":"article","venue":"","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer vision; Artificial intelligence; Computer science; Stereopsis; Collision avoidance; Obstacle avoidance; Colored; Obstacle; Active vision; Perception; Looming; Depth perception; Mobile robot; Collision; Robot; Psychology; Geography","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":[0.0001491991,0.0002674476,0.0001890432,0.0005665274,0.0001893229,0.0003267001,0.0004582308,0.0002476913,0.003490783],"category_scores_gemma":[0.0003654489,0.0001086273,0.0002939961,0.0003796995,0.0002789331,0.0005114553,0.0004163831,0.0002683137,0.0010727],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003793323,"about_ca_system_score_gemma":0.0004368109,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002499949,"about_ca_topic_score_gemma":0.00370425,"domain_scores_codex":[0.9998598,0.00001380921,0.000003842863,0.00002473005,0.00008034817,0.00001750151],"domain_scores_gemma":[0.9998251,0.00003050378,0.0000203617,0.0000384598,0.00007006025,0.00001557644],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001781706,0.00009380725,0.001338577,0.0001278577,0.00004426233,0.0001290533,0.00006488679,0.03300244,0.2765615,0.0556903,0.005287529,0.6274816],"study_design_scores_gemma":[0.00004064275,0.0001753873,0.00380232,0.00001996324,0.00004745578,0.0004175529,0.00004061907,0.8043254,0.1097176,0.03802835,0.04333098,0.00005374112],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01815745,0.00049719,0.9709387,0.0001397301,0.00005705665,0.00002923555,0.00009309824,0.001612493,0.008475048],"genre_scores_gemma":[0.3906669,0.0007297879,0.601413,0.0002056802,0.00009170311,0.00006721605,0.000320246,0.0002455756,0.006259973],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003490783,"threshold_uncertainty_score":0.0116778,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02045792266836624,"score_gpt":0.2705386316393418,"score_spread":0.2500807089709755,"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."}}