{"id":"W2951737368","doi":"10.1007/978-3-030-22368-7_1","title":"The Fractional Harris-Laplace Feature Detector","year":2019,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Image Processing Techniques and Applications","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Laplace transform; Fractional calculus; Computer science; Grayscale; Feature (linguistics); Detector; Artificial intelligence; Pixel; Calculus (dental); Algorithm; Mathematics; Applied mathematics; Mathematical analysis; Telecommunications","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":[0.0003591524,0.0003908571,0.0007168821,0.001112689,0.0002960564,0.001216296,0.0008205742,0.001002166,0.004969832],"category_scores_gemma":[0.001089763,0.0002818978,0.0004323357,0.001268386,0.0003962393,0.001336245,0.0005800356,0.0007061627,0.003078595],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004977757,"about_ca_system_score_gemma":0.0005016042,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007503858,"about_ca_topic_score_gemma":0.0006663459,"domain_scores_codex":[0.9997954,0.0000177687,0.000008411379,0.00004918679,0.0001022014,0.00002696291],"domain_scores_gemma":[0.9997562,0.00006681898,0.00001734908,0.00005169313,0.00009132411,0.00001664622],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001336771,0.00003303948,0.0002161509,0.0001008854,0.00001708306,0.00009624076,0.00002276071,0.00744527,0.04244921,0.03872532,0.008370957,0.9023895],"study_design_scores_gemma":[0.0000369379,0.0002437546,0.001557984,0.00006286297,0.00008311804,0.002199181,0.00004530722,0.7373733,0.0868524,0.06345912,0.1080005,0.00008553466],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006018846,0.002016369,0.9793425,0.0002518468,0.0003480709,0.00002735214,0.00009182117,0.001413126,0.01049012],"genre_scores_gemma":[0.2380354,0.003998306,0.7196046,0.0004478157,0.0004751405,0.00005551717,0.0005103504,0.0002755537,0.03659733],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004969832,"threshold_uncertainty_score":0.01662576,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008630476127952045,"score_gpt":0.2323043797278967,"score_spread":0.2236739035999447,"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."}}