{"id":"W4382450003","doi":"10.1609/aaai.v37i1.25202","title":"Pixel-Wise Warping for Deep Image Stitching","year":2023,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Ministry of Science and ICT, South Korea; National Research Foundation of Korea; National Research Foundation","keywords":"Image warping; Image stitching; Artificial intelligence; Computer vision; Homography; Computer science; Pixel; Parallax; 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":[0.0004697942,0.0009458232,0.0005955572,0.0008083686,0.000230797,0.0004291232,0.001236193,0.000907765,0.002543229],"category_scores_gemma":[0.001264539,0.0005262989,0.0006777175,0.0008141277,0.0005212678,0.001193356,0.000902801,0.001676345,0.0008026646],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004733875,"about_ca_system_score_gemma":0.0005286973,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001953509,"about_ca_topic_score_gemma":0.002792132,"domain_scores_codex":[0.9997112,0.00003605218,0.00001442466,0.00007950659,0.0001221456,0.00003662841],"domain_scores_gemma":[0.9996063,0.0001024108,0.00005965256,0.0001341144,0.00007034986,0.00002721338],"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":[0.00017674,0.00008502248,0.0005760659,0.0001553013,0.00008480562,0.000141272,0.0001095737,0.2461604,0.09970581,0.006111119,0.002813829,0.6438801],"study_design_scores_gemma":[0.00000741561,0.0000646027,0.0002868208,0.000007434219,0.00001008555,0.00009105387,0.00001136588,0.9616179,0.03243601,0.003508664,0.001947102,0.00001145679],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01960636,0.00033573,0.9770442,0.00005240173,0.000027647,0.00004177938,0.00008377464,0.001955608,0.0008526429],"genre_scores_gemma":[0.3044563,0.0005013282,0.6908107,0.0001371853,0.00004529013,0.0001066519,0.0007399293,0.0003589257,0.002843674],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002543229,"threshold_uncertainty_score":0.008507967,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09120065884705537,"score_gpt":0.3474055702057776,"score_spread":0.2562049113587222,"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."}}