{"id":"W4283691409","doi":"10.1002/sdtp.15558","title":"47‐2: Monocular Depth Perception Enhancement Based on Joint Shading/Contrast Model and Motion Parallax (JSM)","year":2022,"lang":"en","type":"article","venue":"SID Symposium Digest of Technical Papers","topic":"Advanced Optical Imaging Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Faurecia (Canada)","funders":"","keywords":"Depth perception; Stereoscopy; Monocular; Parallax; Computer vision; Computer science; Artificial intelligence; Perception; Stereo display; Kinetic depth effect; Stereopsis; Contrast (vision); Binocular disparity; Depth map; Computer graphics (images); Motion (physics); Motion perception; Psychology; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001795428,0.0002627626,0.0003158691,0.00016367,0.0001359775,0.00002077227,0.0002497226,0.0001140157,0.00003983222],"category_scores_gemma":[0.00006487289,0.0002704874,0.0001105077,0.0001916952,0.000176643,0.0001123826,0.0001598379,0.0004739784,0.000004027345],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004571065,"about_ca_system_score_gemma":0.00001438555,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004934804,"about_ca_topic_score_gemma":0.000003516845,"domain_scores_codex":[0.9984583,0.00002468784,0.00037116,0.0003950462,0.0003899415,0.0003608218],"domain_scores_gemma":[0.999324,0.00005783139,0.00006554565,0.0004400443,0.00002618123,0.00008635395],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001798294,0.00006414267,0.0001013987,0.00002990626,0.000008307994,0.000002687772,0.00001373264,0.4209565,0.5775033,0.000522423,0.0000382399,0.0007413453],"study_design_scores_gemma":[0.001346847,0.0008820525,0.006317985,0.0001327721,0.00009052113,0.00001615628,0.0002023049,0.8513624,0.1364967,0.00157011,0.0006403353,0.0009417735],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6360166,0.0006490269,0.07092991,0.008667883,0.000609355,0.002862153,0.000117351,0.007267717,0.27288],"genre_scores_gemma":[0.9913651,0.0001232288,0.008036509,0.0001402048,0.00001279655,0.0002134751,0.00002034397,0.00005174504,0.00003654837],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4410066,"threshold_uncertainty_score":0.9999747,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01177564763164002,"score_gpt":0.2210616293491644,"score_spread":0.2092859817175244,"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."}}