{"id":"W2946757886","doi":"10.1109/tim.2019.2905906","title":"Object Recognition From Haptic Glance at Visually Salient Locations","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Tactile and Sensory Interactions","field":"Neuroscience","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Computer vision; Computer science; Cognitive neuroscience of visual object recognition; Pattern recognition (psychology); Haptic technology; Support vector machine; 3D single-object recognition; Object (grammar); Classifier (UML); Tactile sensor; Orientation (vector space); Robot; Mathematics","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":[0.0002732461,0.0003104805,0.0005052311,0.0004499453,0.0001128638,0.0005587027,0.000309548,0.0003679742,0.001019028],"category_scores_gemma":[0.001377469,0.0001335476,0.0003629634,0.0002766357,0.0003655151,0.0007957424,0.0005581888,0.0002518256,0.0005183104],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001549754,"about_ca_system_score_gemma":0.0001516039,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006468379,"about_ca_topic_score_gemma":0.000713522,"domain_scores_codex":[0.9997801,0.00002555657,0.000009500576,0.00004270543,0.0001156105,0.00002643364],"domain_scores_gemma":[0.9995496,0.0001829159,0.00009547704,0.00008821034,0.00006544623,0.00001845459],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004830791,0.0001044761,0.004015607,0.0002961716,0.0000455208,0.0004076727,0.000211212,0.05082158,0.5636073,0.0008660145,0.0007865729,0.3783547],"study_design_scores_gemma":[0.00002113111,0.0007544068,0.06168203,0.0000581495,0.00005775853,0.001329348,0.0002464212,0.6051502,0.3255281,0.003003029,0.00208857,0.00008084724],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7096788,0.0006951801,0.2843773,0.00009182208,0.00006949189,0.00004285307,0.0001652013,0.001129667,0.003749708],"genre_scores_gemma":[0.9675968,0.0002408154,0.03078456,0.00003008374,0.00001624153,0.00001469565,0.0001621423,0.00003122527,0.001123466],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001019028,"threshold_uncertainty_score":0.003408968,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06817288887128843,"score_gpt":0.2782500167222206,"score_spread":0.2100771278509322,"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."}}