{"id":"W1992779583","doi":"10.1109/crv.2014.25","title":"Grid Seams: A Fast Superpixel Algorithm for Real-Time Applications","year":2014,"lang":"en","type":"article","venue":"","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pixel; Computer science; Artificial intelligence; Computer vision; Grid; Seam carving; Algorithm; Constraint (computer-aided design); Segmentation; Representation (politics); Image segmentation; Pattern recognition (psychology); 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002067231,0.0001148489,0.0001400475,0.00006167339,0.0001335719,0.000091854,0.0006372841,0.00004754358,0.00001661367],"category_scores_gemma":[0.00002544718,0.00009839604,0.00007209179,0.0002685645,0.00003438897,0.0004806824,0.0001458544,0.00005387422,0.0001263911],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002820835,"about_ca_system_score_gemma":0.00002578779,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001339156,"about_ca_topic_score_gemma":6.388719e-7,"domain_scores_codex":[0.999085,0.00001756513,0.0001658148,0.0003567078,0.0001301842,0.000244704],"domain_scores_gemma":[0.9989942,0.0001484851,0.00004136556,0.0005871783,0.0001394246,0.00008928819],"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":[7.706107e-7,0.00003394742,0.00000483261,0.000005667667,0.00000413969,2.063748e-7,0.00004199229,8.235584e-7,0.003808707,0.03586538,0.004284626,0.9559489],"study_design_scores_gemma":[0.0003431291,0.0002690215,0.00005772288,0.00001080493,0.000007678094,0.00001260935,0.00001245228,0.2129556,0.1017692,0.03273527,0.6514649,0.0003615421],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000007999784,0.00001557716,0.9886762,0.0004488386,0.0000414086,0.0005059178,0.00001068301,0.000886143,0.00940718],"genre_scores_gemma":[0.0004264278,0.00004314124,0.99499,0.0005368617,0.0002234635,0.0003430211,0.00001406851,0.00001304555,0.00341],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9555874,"threshold_uncertainty_score":0.4012474,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008821513797139087,"score_gpt":0.2725982425201988,"score_spread":0.2637767287230597,"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."}}