{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006373704,0.0007015437,0.001317431,0.001086786,0.000299227,0.0007498118,0.001089893,0.0009648965,0.0009903258],"category_scores_gemma":[0.0008185675,0.0002911474,0.0009884438,0.001278006,0.0009460105,0.001669156,0.001118435,0.0007312797,0.0006166059],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003786639,"about_ca_system_score_gemma":0.0003749672,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001108813,"about_ca_topic_score_gemma":0.0009158934,"domain_scores_codex":[0.9993644,0.00008680557,0.00002777099,0.0002027949,0.0002355135,0.00008271812],"domain_scores_gemma":[0.9996918,0.0000666015,0.00005079659,0.00008297154,0.00008517333,0.00002259418],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001893973,0.00008808483,0.001252185,0.0003589692,0.0001290566,0.0003357796,0.0001548177,0.06156688,0.1670223,0.02116894,0.002872806,0.7448607],"study_design_scores_gemma":[0.00002102945,0.0002621545,0.004139112,0.00003361038,0.00009092909,0.0008871144,0.0000760254,0.891776,0.07375524,0.0195095,0.009335878,0.0001133747],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007274066,0.0004692301,0.9908559,0.00004859193,0.00004729126,0.00003021703,0.00003475519,0.0006964295,0.0005435288],"genre_scores_gemma":[0.304997,0.0008996821,0.6918075,0.0001654493,0.0001073589,0.00008819101,0.0002700211,0.0001403734,0.001524396],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001317431,"threshold_uncertainty_score":0.003370762,"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."}}