{"id":"W1551941265","doi":"10.1109/ccece.2015.7129342","title":"Accurate seed points classification using invariant moments & neural network","year":2015,"lang":"en","type":"article","venue":"","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Artificial intelligence; Segmentation; Pattern recognition (psychology); Artificial neural network; Computer science; Image segmentation; Support vector machine; Feature extraction; Computer vision","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":[0.000437936,0.0005296963,0.0005735748,0.001188152,0.0003159142,0.0006109713,0.0005460758,0.0008578811,0.0011053],"category_scores_gemma":[0.001197751,0.0002565802,0.0004269927,0.0006941361,0.0003842544,0.0008212242,0.000352205,0.0005470192,0.0005528448],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005449535,"about_ca_system_score_gemma":0.0003870492,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003059371,"about_ca_topic_score_gemma":0.002905992,"domain_scores_codex":[0.9997253,0.00004170595,0.00001750416,0.00007081504,0.0001041701,0.00004055559],"domain_scores_gemma":[0.999585,0.0001230019,0.00007640707,0.00004609441,0.0001481509,0.00002149688],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004176766,0.0001714823,0.003209155,0.00008915358,0.00006406775,0.0002038878,0.000066173,0.138336,0.06977722,0.001854768,0.003293426,0.782517],"study_design_scores_gemma":[0.000006286571,0.00004217435,0.001291965,0.000005418779,0.00001010808,0.00006058177,0.000009273094,0.9865236,0.01093685,0.0006507926,0.0004533418,0.000009625367],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.09125562,0.0005407934,0.9039024,0.0001946002,0.00009125654,0.00006369676,0.00006853741,0.001831452,0.00205161],"genre_scores_gemma":[0.7291225,0.0003547447,0.2666034,0.00009062554,0.00006575385,0.0000601353,0.0002074162,0.00008348084,0.003412008],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003059371,"threshold_uncertainty_score":0.006083071,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1615108993836183,"score_gpt":0.3348552204838128,"score_spread":0.1733443211001946,"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."}}