{"id":"W1571828317","doi":"10.1109/iscas.2006.1693415","title":"3D position sensing using a Hopfield neural network stereo matching algorithm","year":2006,"lang":"en","type":"article","venue":"","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Sobel operator; Artificial intelligence; Correspondence problem; Artificial neural network; Computer science; Matching (statistics); Computer vision; Position (finance); Image (mathematics); Point (geometry); Algorithm; Function (biology); Enhanced Data Rates for GSM Evolution; Operator (biology); Pattern recognition (psychology); Hopfield network; Edge detection; Mathematics; Image processing; Geometry","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0005069625,0.0003675517,0.000437614,0.0005040606,0.0003753915,0.0004554871,0.0009307673,0.0007907354,0.001678494],"category_scores_gemma":[0.0007470876,0.0002813235,0.0004090257,0.0004626543,0.00036964,0.0008800757,0.0005720597,0.0003658581,0.0003099269],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001077479,"about_ca_system_score_gemma":0.0008675514,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008028079,"about_ca_topic_score_gemma":0.008582308,"domain_scores_codex":[0.9997496,0.00003187356,0.00001371587,0.00006204779,0.0001183984,0.00002443432],"domain_scores_gemma":[0.9998406,0.00004550087,0.00002309873,0.00002228636,0.00005627815,0.00001223755],"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":[0.0001881295,0.00007790508,0.0008970168,0.00008438864,0.00006521746,0.0001346355,0.000119577,0.3273853,0.05028475,0.0115287,0.001323355,0.6079111],"study_design_scores_gemma":[0.00001574549,0.0000327627,0.0002512978,0.000005888207,0.0000104656,0.00004771989,0.000007271007,0.9891887,0.006790209,0.00278977,0.0008489023,0.00001123628],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01191003,0.00009929308,0.9858497,0.00004369444,0.00001777309,0.0000304435,0.00002339772,0.0003791168,0.001646477],"genre_scores_gemma":[0.3809161,0.0001483071,0.6156631,0.0001098294,0.00002781592,0.00009906269,0.0000640276,0.00004021967,0.00293159],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008028079,"threshold_uncertainty_score":0.01596272,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01246754146531552,"score_gpt":0.2630974254436427,"score_spread":0.2506298839783272,"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."}}