{"id":"W1995949881","doi":"10.1109/icip.2011.6116695","title":"Semi-automatic 2D to 3D image conversion using scale-space Random Walks and a graph cuts based depth prior","year":2011,"lang":"en","type":"article","venue":"","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Stereoscopy; Computer science; Computer vision; Artificial intelligence; Random walk; Graph; Image segmentation; Image (mathematics); Segmentation; Depth map; 2D to 3D conversion; Random walker algorithm; Mathematics; Theoretical computer science","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":[],"consensus_categories":[],"category_scores_codex":[0.0002262606,0.0001653875,0.000209562,0.00019643,0.0001756608,0.0001202624,0.0003341355,0.00003435668,0.00008407745],"category_scores_gemma":[0.0000400716,0.0001351432,0.00005333358,0.0003827205,0.00005955654,0.0007888869,0.0002581916,0.00008943952,0.00004519801],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000269984,"about_ca_system_score_gemma":0.0000418916,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006886647,"about_ca_topic_score_gemma":0.00001016155,"domain_scores_codex":[0.9988095,0.00006291912,0.0001941619,0.0004153456,0.0002187276,0.0002993861],"domain_scores_gemma":[0.9991246,0.000087473,0.00006899178,0.0004107203,0.00007530401,0.0002328807],"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":[0.0004168107,0.0005603182,0.0142675,0.0004388159,0.00006523954,0.0002171589,0.01718922,0.0002928946,0.2329191,0.001346548,0.004356226,0.7279302],"study_design_scores_gemma":[0.002079306,0.00005685216,0.001898988,0.0001092479,0.00001073001,0.00002328758,0.000130801,0.9623143,0.03248546,0.0002341764,0.0004066453,0.0002502138],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07707262,0.00003738982,0.9195329,0.0002523795,0.0001336519,0.000276751,4.695228e-7,0.0002384115,0.002455414],"genre_scores_gemma":[0.3293074,0.000003845242,0.6693661,0.001177973,0.000007609477,0.000003349637,2.872169e-7,0.000009454179,0.0001239784],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9620214,"threshold_uncertainty_score":0.551098,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01981602043330788,"score_gpt":0.263533487714225,"score_spread":0.2437174672809171,"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."}}