{"id":"W2115524834","doi":"10.1109/icpr.2008.4761620","title":"Moving object segmentation using the 9/7–10/8 dual-tree complex filter bank","year":2008,"lang":"en","type":"article","venue":"Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Complex wavelet transform; Artificial intelligence; Computer vision; Segmentation; Computer science; Filter bank; Wavelet; Filter (signal processing); Tree (set theory); Pattern recognition (psychology); Image segmentation; Edge detection; Wavelet transform; Discrete wavelet transform; Mathematics; Image processing; Image (mathematics)","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001340786,0.001267893,0.0008047788,0.001424262,0.001236463,0.002998472,0.002842393,0.0004419489,0.00944177],"category_scores_gemma":[0.000504095,0.001171467,0.0005149917,0.0007702359,0.0004156899,0.004020716,0.0006884157,0.001382856,0.002184929],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007571184,"about_ca_system_score_gemma":0.0002697134,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002426363,"about_ca_topic_score_gemma":0.00004542956,"domain_scores_codex":[0.9915725,0.0001287199,0.001722353,0.002290423,0.003116628,0.001169397],"domain_scores_gemma":[0.9908078,0.0003060763,0.001632592,0.0003990861,0.006442509,0.0004119502],"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":[0.00162681,0.002391554,0.01117778,0.000385707,0.001731761,0.0001790367,0.008368476,0.00003594835,0.1981699,0.01422936,0.01487691,0.7468268],"study_design_scores_gemma":[0.02441936,0.003991932,0.04903793,0.009902486,0.0008816268,0.006477884,0.009022136,0.6378282,0.1430949,0.09239285,0.01001279,0.01293792],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4396101,0.00002175252,0.3667622,0.01027471,0.004506792,0.002060525,0.0008724242,0.00106449,0.1748271],"genre_scores_gemma":[0.9700704,0.0003866001,0.0184935,0.005417665,0.00176132,0.0004727033,0.001234943,0.0001469325,0.002015928],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7338889,"threshold_uncertainty_score":0.9990735,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.201838373321676,"score_gpt":0.3423718941395668,"score_spread":0.1405335208178908,"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."}}