{"id":"W2273269588","doi":"10.1016/j.sigpro.2016.01.016","title":"Rotation invariant HOG for object localization in web images","year":2016,"lang":"en","type":"article","venue":"Signal Processing","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Artificial intelligence; Invariant (physics); Histogram; Computer vision; Robustness (evolution); Histogram of oriented gradients; Rotation (mathematics); Computer science; Pattern recognition (psychology); Cognitive neuroscience of visual object recognition; Mathematics; Feature extraction; 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":[0.0001970719,0.000386708,0.0005381474,0.001207044,0.0002024869,0.0004364686,0.0004911345,0.0003506431,0.003240454],"category_scores_gemma":[0.0005313366,0.0002106325,0.0003010735,0.001127442,0.0002245853,0.0006061722,0.000548256,0.0004315659,0.00173323],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003103811,"about_ca_system_score_gemma":0.0004375832,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003791836,"about_ca_topic_score_gemma":0.006092429,"domain_scores_codex":[0.99985,0.00001710486,0.000005968978,0.00003454799,0.00005811131,0.00003413276],"domain_scores_gemma":[0.9998823,0.00001951267,0.00001086297,0.00003079206,0.00004283559,0.0000137216],"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":[0.0003445725,0.0001792594,0.001416397,0.0001872505,0.00005756976,0.0001642647,0.00003183558,0.02384655,0.1667107,0.002871845,0.009610141,0.7945796],"study_design_scores_gemma":[0.00003549196,0.0001864601,0.007644184,0.00003218714,0.00005274663,0.000333048,0.00008376695,0.8924051,0.08560336,0.005614846,0.007977712,0.00003115505],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08563995,0.002120643,0.9019285,0.0002320909,0.0001736776,0.000155026,0.00101078,0.004764916,0.003974395],"genre_scores_gemma":[0.5733815,0.001928245,0.4100269,0.0002053857,0.0001206895,0.0001274353,0.002525556,0.0003442612,0.01133991],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003791836,"threshold_uncertainty_score":0.01084042,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01878560405998516,"score_gpt":0.2912272856869152,"score_spread":0.2724416816269301,"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."}}