{"id":"W1875662944","doi":"10.1007/978-3-642-31298-4_21","title":"Dental X-Ray Image Segmentation and Object Detection Based on Phase Congruency","year":2012,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Artificial intelligence; Computer science; Computer vision; Phase congruency; Segmentation; Image segmentation; Translation (biology); Invariant (physics); Rotation (mathematics); Pattern recognition (psychology); Image (mathematics); 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":[0.0005481853,0.0005419628,0.0009421347,0.001894437,0.0003206105,0.001285613,0.0008866155,0.0009776377,0.0059297],"category_scores_gemma":[0.001305849,0.0007749958,0.0007614772,0.001711219,0.000570912,0.001153366,0.0008287071,0.0006706461,0.003022382],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002737483,"about_ca_system_score_gemma":0.0006479044,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004300048,"about_ca_topic_score_gemma":0.0006387076,"domain_scores_codex":[0.9995289,0.00004218884,0.00002860984,0.000116233,0.0002491632,0.00003492331],"domain_scores_gemma":[0.9996001,0.0001781419,0.00003759376,0.00004628316,0.0001188662,0.00001897268],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003289276,0.00006441422,0.000848491,0.0004612347,0.00005278763,0.000172625,0.0001264076,0.004501523,0.3611023,0.007158502,0.001730835,0.6234519],"study_design_scores_gemma":[0.0001233342,0.0005058137,0.01421109,0.0001614185,0.0003931089,0.004953046,0.0001934783,0.3382649,0.5863071,0.01693644,0.03779462,0.0001555941],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01215965,0.0009002438,0.9824857,0.00009354731,0.00007067243,0.00008866964,0.00008604604,0.0007245212,0.003390967],"genre_scores_gemma":[0.08866625,0.001487758,0.9046451,0.0001058022,0.00008683358,0.0001063938,0.0002703822,0.0002451834,0.004386256],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0059297,"threshold_uncertainty_score":0.01983684,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01293697506479827,"score_gpt":0.2843355880377925,"score_spread":0.2713986129729942,"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."}}