{"id":"W2133161341","doi":"10.1109/isspit.2007.4458129","title":"An Image Normalization Technique based on Geometric Properties of Image Feature Points","year":2007,"lang":"en","type":"article","venue":"","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Normalization (sociology); Artificial intelligence; Feature extraction; Pattern recognition (psychology); Computer vision; Computer science; Robustness (evolution); Image processing; Wavelet; Wavelet transform; 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":[],"consensus_categories":[],"category_scores_codex":[0.0009194519,0.000141483,0.0001425801,0.0006511601,0.00007573424,0.0001049461,0.0006835113,0.0001079458,0.00002909429],"category_scores_gemma":[0.0001090359,0.0001055571,0.00005818197,0.001815805,0.00007788709,0.000857683,0.00005395032,0.0001296006,0.00001614059],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005115854,"about_ca_system_score_gemma":0.00005693308,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001687927,"about_ca_topic_score_gemma":0.000001348108,"domain_scores_codex":[0.9987609,0.00005462755,0.0002585258,0.0003045993,0.0004007964,0.0002204854],"domain_scores_gemma":[0.998672,0.00003010132,0.0001411144,0.0006615228,0.0004172699,0.00007799452],"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":[0.00003287488,0.0002850351,0.0001598479,0.00005888064,0.000002834932,0.000005528681,0.00005438089,0.000001012663,0.9654529,0.006430036,0.0005178829,0.02699875],"study_design_scores_gemma":[0.0001175145,0.0002358217,0.002190275,0.00003257526,0.000002432963,0.000004385715,0.00001453157,0.01438428,0.9821854,0.0002136999,0.0004733934,0.0001457094],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001033434,0.00001888219,0.9904692,0.000528487,0.00003559479,0.000387683,0.000001890692,0.0005528737,0.006971941],"genre_scores_gemma":[0.6000061,0.000004937464,0.3994111,0.0003045515,0.00001584725,0.00001558249,0.000004960746,0.000009091756,0.0002278484],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5989727,"threshold_uncertainty_score":0.4304495,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01309557990935945,"score_gpt":0.2551290444707275,"score_spread":0.242033464561368,"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."}}