{"id":"W2100968369","doi":"10.1109/tpami.2005.220","title":"Efficient shape matching using shape contexts","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":458,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Shape analysis (program analysis); Heat kernel signature; Active shape model; Artificial intelligence; Shape context; Computer science; Matching (statistics); Computer vision; Pattern recognition (psychology); Vector quantization; Mathematics; Image (mathematics); Segmentation","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":[0.0005038269,0.0008323003,0.001410735,0.002644974,0.0009871569,0.001618635,0.001674308,0.001110166,0.005302539],"category_scores_gemma":[0.003447483,0.0004894135,0.0008567105,0.002862963,0.000858397,0.003127602,0.002697504,0.0009932773,0.002471938],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004760782,"about_ca_system_score_gemma":0.0008550533,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002038883,"about_ca_topic_score_gemma":0.003738307,"domain_scores_codex":[0.9988315,0.00014054,0.00007305892,0.0003118781,0.0005245314,0.0001185353],"domain_scores_gemma":[0.9986972,0.0003511892,0.0001283196,0.0004518381,0.0002853798,0.0000860865],"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":[0.0006889964,0.0001330444,0.002261887,0.0002182907,0.00005479828,0.0002636951,0.0002089544,0.02580831,0.1317686,0.02584721,0.005958009,0.8067882],"study_design_scores_gemma":[0.0001238176,0.0006007691,0.004080005,0.00006451065,0.0000811477,0.001420537,0.0004432741,0.7796981,0.1128847,0.07834683,0.02211554,0.0001406829],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.100973,0.001187098,0.8862135,0.0002078574,0.0001289283,0.0002146268,0.0003767434,0.004482117,0.006216183],"genre_scores_gemma":[0.4128723,0.0005205251,0.5825346,0.0001855345,0.00007423592,0.000119017,0.0008408776,0.0004366155,0.002416273],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005302539,"threshold_uncertainty_score":0.01773882,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02776617728988811,"score_gpt":0.2924933018035573,"score_spread":0.2647271245136691,"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."}}