{"id":"W1975304868","doi":"10.1023/b:vlsi.0000017006.75431.c7","title":"A Low Power Architecture for HASM Motion Tracking","year":2004,"lang":"en","type":"article","venue":"The Journal of VLSI Signal Processing Systems for Signal Image and Video Technology","topic":"Video Coding and Compression Technologies","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Affine transformation; Motion estimation; Motion compensation; Motion vector; Mesh networking; Computer vision; 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":[0.000207263,0.0004935361,0.0002852494,0.000895744,0.0006877659,0.0006364639,0.0014517,0.0005339602,0.01348641],"category_scores_gemma":[0.0003965142,0.0003052083,0.000229349,0.0007797171,0.0002031676,0.000873696,0.00060989,0.0004205576,0.00279849],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005564632,"about_ca_system_score_gemma":0.000487752,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001725432,"about_ca_topic_score_gemma":0.006350155,"domain_scores_codex":[0.9998018,0.00002269346,0.00001293882,0.00004249773,0.00008952485,0.00003047717],"domain_scores_gemma":[0.9997926,0.0000516889,0.00001800231,0.00004399802,0.00008003764,0.00001362304],"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":[0.0006461727,0.0001508883,0.001493474,0.0003210397,0.00009229486,0.000255841,0.0001574592,0.00610523,0.2331243,0.01326455,0.0139447,0.7304441],"study_design_scores_gemma":[0.000234113,0.002367248,0.003645721,0.0001558493,0.0003354861,0.001559397,0.0001720701,0.3990371,0.4634897,0.01683781,0.1120351,0.0001302615],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08742923,0.003173393,0.8686534,0.0004803016,0.0005894942,0.0002231772,0.0003348553,0.009903449,0.02921275],"genre_scores_gemma":[0.5996981,0.0006312409,0.3534301,0.0007193566,0.0001851475,0.0001304883,0.0005594941,0.0002464598,0.04439959],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01348641,"threshold_uncertainty_score":0.04511654,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01640482565926054,"score_gpt":0.2644000435190492,"score_spread":0.2479952178597887,"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."}}