{"id":"W3092039676","doi":"10.1109/lgrs.2020.3027096","title":"Remote Sensing Image Registration Based on Local Affine Constraint With Circle Descriptor","year":2020,"lang":"en","type":"article","venue":"IEEE Geoscience and Remote Sensing Letters","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Northwest University; National Natural Science Foundation of China","keywords":"Subpixel rendering; Affine transformation; Scale-invariant feature transform; Image registration; Artificial intelligence; Pattern recognition (psychology); Computer science; Feature (linguistics); Feature extraction; Computer vision; Constraint (computer-aided design); Matching (statistics); Image matching; Transformation (genetics); Mathematics; Image (mathematics); Pixel","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":[0.000316209,0.0002519265,0.000243042,0.0001179802,0.0003444786,0.0003649302,0.0002833079,0.00006546151,7.560644e-7],"category_scores_gemma":[0.0001142886,0.0002211527,0.00005567495,0.0006973566,0.000789873,0.0006353104,0.00005376982,0.0003059633,0.000006974839],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007548174,"about_ca_system_score_gemma":0.0001063689,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001817196,"about_ca_topic_score_gemma":0.00001252229,"domain_scores_codex":[0.9978424,0.00008428027,0.0002642471,0.000838919,0.0004977859,0.0004723709],"domain_scores_gemma":[0.9989401,0.00009869566,0.0001621201,0.0004566849,0.0001106984,0.0002316853],"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":[0.00003918995,0.000003723689,8.164191e-7,0.00001736059,0.000002595143,0.0003168453,0.0001825643,0.0001408712,0.2530359,0.0000102866,0.0002930328,0.7459568],"study_design_scores_gemma":[0.0003533541,0.0003933326,0.00009100797,0.0002160773,0.000008576926,0.0001881268,0.00005466474,0.8493676,0.1481069,0.0001789726,0.0007034445,0.0003380002],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02107612,0.0000116249,0.9560601,0.0218085,0.0001733055,0.0002110956,0.000001598953,0.0003715642,0.0002861434],"genre_scores_gemma":[0.3860566,0.000007860092,0.5952159,0.01859702,0.00009510248,1.143731e-8,0.000001223616,0.00001283719,0.00001339197],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8492267,"threshold_uncertainty_score":0.9018344,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01739943127894161,"score_gpt":0.2380266177765105,"score_spread":0.2206271864975689,"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."}}