{"id":"W2069477605","doi":"10.1109/cicc.2006.320945","title":"ViPro: Focal-Plane Spatially-Oversampling CMOS Image Compression Sensor","year":2006,"lang":"en","type":"article","venue":"","topic":"CCD and CMOS Imaging Sensors","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Oversampling; Pixel; Quantization (signal processing); Discrete cosine transform; Cardinal point; Computer science; Artificial intelligence; Computer vision; Data compression; CMOS; Frame rate; Algorithm; Electronic engineering; Engineering; Image (mathematics); Optics; Physics","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":[0.0001727794,0.000217065,0.0003162349,0.0002954511,0.000170632,0.0004920601,0.0009738152,0.0003806366,0.002492986],"category_scores_gemma":[0.0003831404,0.0001734631,0.0001334068,0.0003527848,0.0002474225,0.0003659868,0.0004520378,0.0004068779,0.0006891342],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005424602,"about_ca_system_score_gemma":0.0005056532,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001077488,"about_ca_topic_score_gemma":0.001957,"domain_scores_codex":[0.9996167,0.00001585873,0.00001138835,0.00008022973,0.0002447033,0.00003089901],"domain_scores_gemma":[0.9998355,0.00001793815,0.00002909259,0.00002678438,0.00007138465,0.00001931991],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002700854,0.00005636334,0.001430838,0.0002110339,0.00002270809,0.0001948293,0.00005689442,0.003348676,0.8577746,0.004982954,0.01031518,0.1213359],"study_design_scores_gemma":[0.00006403888,0.000512302,0.006397748,0.00002044028,0.00003305966,0.001916349,0.00005618727,0.05482083,0.8972915,0.0008497908,0.03799092,0.00004672486],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3540511,0.002093864,0.5798401,0.001019354,0.0004558721,0.0004658677,0.002377957,0.01396183,0.04573403],"genre_scores_gemma":[0.7433433,0.000455643,0.231856,0.000453807,0.0000851784,0.0001020493,0.001539269,0.0001257291,0.02203893],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002492986,"threshold_uncertainty_score":0.008339882,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005105835870121395,"score_gpt":0.1945105519231172,"score_spread":0.1894047160529958,"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."}}