{"id":"W2523941652","doi":"10.2316/journal.206.2016.5.206-4706","title":"GENERIC OBJECT RECOGNITION BASED ON FEATURE FUSION IN ROBOT PERCEPTION","year":2016,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Computer vision; Computer science; Feature (linguistics); Cognitive neuroscience of visual object recognition; Pattern recognition (psychology); Object (grammar); Fusion; Robot; Invariant (physics); Perception; Point cloud; 3D single-object recognition; Mathematics; Psychology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002659604,0.00007791721,0.00009560995,0.0003469956,0.00002598207,0.00007892659,0.0002216601,0.00005348491,0.000007800853],"category_scores_gemma":[0.0001157653,0.00005278952,0.00004496777,0.0001305678,0.0000152042,0.0007564197,0.00003438236,0.00009206316,0.000004598125],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000128351,"about_ca_system_score_gemma":0.00003462423,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001555374,"about_ca_topic_score_gemma":0.000001196541,"domain_scores_codex":[0.9991718,0.00004820565,0.0002400479,0.0001165232,0.0003461232,0.00007732671],"domain_scores_gemma":[0.9992505,0.00008416951,0.0002483921,0.00007787302,0.0003049865,0.00003409976],"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":[0.00004636142,0.00007468224,0.0006322667,0.000004452545,0.000007039609,0.00003009389,0.00007320249,0.002081408,0.03389712,0.0006790374,0.0001865723,0.9622878],"study_design_scores_gemma":[0.004693537,0.00181357,0.2564282,0.002523863,0.00002471849,0.0004423591,0.00005791034,0.6169601,0.05217811,0.06287303,0.001348117,0.0006565066],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02332992,0.00003218043,0.9708301,0.005274289,0.0003458619,0.00005414039,0.000001787981,0.00003217462,0.00009956427],"genre_scores_gemma":[0.7872165,0.0002854731,0.2120546,0.0003001004,0.0001118115,0.000001189434,0.000003009625,0.00000457946,0.00002272006],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9616312,"threshold_uncertainty_score":0.2152694,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01848435377115579,"score_gpt":0.2816493050262293,"score_spread":0.2631649512550734,"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."}}