{"id":"W2158483412","doi":"10.1109/camp.1995.521015","title":"Low level segmentation using CMOS smart hexagonal image sensor","year":2002,"lang":"en","type":"article","venue":"","topic":"CCD and CMOS Imaging Sensors","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Very-large-scale integration; Image sensor; Pixel; Computer vision; Artificial intelligence; Image processing; Intelligent sensor; Image segmentation; Pyramid (geometry); Computer hardware; Segmentation; Embedded system; Wireless sensor network; Image (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":[0.0001106318,0.0001450497,0.0002447018,0.0001975162,0.0001496992,0.0003908489,0.0005367885,0.0003920861,0.001082597],"category_scores_gemma":[0.0002972704,0.0001723282,0.0001370054,0.0002153737,0.000236536,0.000485336,0.0003731216,0.0002085356,0.0005777233],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003810646,"about_ca_system_score_gemma":0.0001912146,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000375852,"about_ca_topic_score_gemma":0.0006641292,"domain_scores_codex":[0.9997838,0.00002210762,0.00001056153,0.00005566714,0.0001081242,0.0000197074],"domain_scores_gemma":[0.9997998,0.00003617383,0.00004452003,0.00003434203,0.000065105,0.00002002586],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000146494,0.00002215645,0.000704607,0.0000923261,0.000006595767,0.0000663572,0.00004949803,0.002850729,0.9413578,0.002799689,0.000904446,0.05099927],"study_design_scores_gemma":[0.0000375378,0.0003450741,0.003215524,0.00001230465,0.00002107605,0.0005467443,0.00006141647,0.09121868,0.8899214,0.00157095,0.01300701,0.00004228115],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.319795,0.0009560167,0.6636369,0.0004570778,0.0001673923,0.000138515,0.0005234983,0.003902994,0.01042275],"genre_scores_gemma":[0.6094332,0.0002732934,0.385969,0.0002242321,0.00003050875,0.00004015798,0.0001883319,0.0000540725,0.003787206],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001082597,"threshold_uncertainty_score":0.003621638,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03648453461509579,"score_gpt":0.2320802865477472,"score_spread":0.1955957519326514,"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."}}