{"id":"W4310153928","doi":"10.3390/s22239209","title":"3D Object Recognition Using Fast Overlapped Block Processing Technique","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Computational complexity theory; Cognitive neuroscience of visual object recognition; Block (permutation group theory); Artificial intelligence; Benchmark (surveying); Object (grammar); Feature extraction; Support vector machine; 3D single-object recognition; Object detection; Computation; Noise (video); Feature (linguistics); Pattern recognition (psychology); Image processing; Computer vision; Image (mathematics); Algorithm; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0004247806,0.0008079642,0.0008518298,0.001759586,0.0003537867,0.0008627636,0.0008712947,0.0007298552,0.002667612],"category_scores_gemma":[0.001092329,0.0003678341,0.001014426,0.001521295,0.0003182243,0.001251814,0.0009072713,0.0006736757,0.001927497],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003313127,"about_ca_system_score_gemma":0.0007274148,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002105619,"about_ca_topic_score_gemma":0.002188801,"domain_scores_codex":[0.9992686,0.00007344794,0.00003805207,0.0001407193,0.0004094774,0.00006976876],"domain_scores_gemma":[0.999522,0.0001081075,0.00006923042,0.0001024568,0.0001744148,0.00002376947],"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":[0.0002849846,0.0000903739,0.0007314176,0.0001660785,0.00007160327,0.0002301245,0.0001019929,0.03674443,0.1622754,0.004032196,0.003481019,0.7917904],"study_design_scores_gemma":[0.00002314912,0.0002176566,0.001731324,0.00001945876,0.00004483604,0.0007488562,0.0000612371,0.8919861,0.09213962,0.003042893,0.009936593,0.0000482447],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009460757,0.0003355087,0.9880728,0.00005682915,0.00004634175,0.00004775924,0.00009041298,0.001033431,0.000856217],"genre_scores_gemma":[0.1399326,0.0007785458,0.8554064,0.00009111485,0.00006864604,0.0001339646,0.0006832386,0.0001419707,0.002763588],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002667612,"threshold_uncertainty_score":0.008924007,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03050720205497302,"score_gpt":0.2650796569095968,"score_spread":0.2345724548546237,"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."}}