{"id":"W2131249529","doi":"10.1109/icassp.2008.4517839","title":"Logo and trademark detection in images using Color Wavelet Co-occurrence Histograms","year":2008,"lang":"en","type":"article","venue":"Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Histogram; Artificial intelligence; Histogram matching; Color histogram; Pattern recognition (psychology); Computer vision; Image histogram; Histogram equalization; Mathematics; Color normalization; Adaptive histogram equalization; Color image; Computer science; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005257016,0.0003014127,0.000618493,0.006045401,0.0002887702,0.001157584,0.0004992954,0.000507321,0.001769128],"category_scores_gemma":[0.002237408,0.0001665695,0.0004684656,0.003902329,0.0003491481,0.001812665,0.0005306602,0.000400731,0.001119987],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000389003,"about_ca_system_score_gemma":0.0003724127,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00368671,"about_ca_topic_score_gemma":0.004250989,"domain_scores_codex":[0.9995472,0.00004579953,0.00003033985,0.00009191009,0.0002150912,0.0000696533],"domain_scores_gemma":[0.9991565,0.0002563019,0.0001532252,0.0001312236,0.0002554724,0.00004734728],"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":[0.0006016601,0.0002497947,0.01184426,0.0003156775,0.0001078181,0.0003457568,0.0001534651,0.007880068,0.1403791,0.002823557,0.004511713,0.8307873],"study_design_scores_gemma":[0.0001016624,0.0004052891,0.08212545,0.00006513955,0.0002375214,0.002370114,0.0006138702,0.5742475,0.3170612,0.00794102,0.01464064,0.0001906546],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3083411,0.001363444,0.6759205,0.0002190983,0.0001068279,0.0002703297,0.001410918,0.006795071,0.00557271],"genre_scores_gemma":[0.6652237,0.0008038225,0.3294719,0.00008808193,0.00009540991,0.0001076284,0.001664424,0.0002137968,0.002331278],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006045401,"threshold_uncertainty_score":0.007330477,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0614551676123225,"score_gpt":0.2977465540633896,"score_spread":0.2362913864510671,"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."}}