{"id":"W2128975845","doi":"10.1109/crv.2005.61","title":"Photometric Stereo via Locality Sensitive High-Dimension Hashing","year":2005,"lang":"en","type":"article","venue":"","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Locality-sensitive hashing; Brightness; Computer vision; Artificial intelligence; Hash function; Computer science; Photometric stereo; Orientation (vector space); Object (grammar); Dimension (graph theory); Computation; Point (geometry); Calibration; Locality; Image (mathematics); Hash table; Mathematics; Algorithm; Physics; Geometry; Astronomy","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":[0.0006846286,0.0003558529,0.0006737569,0.0008971517,0.0005865873,0.0006482169,0.001119206,0.0005752249,0.002976198],"category_scores_gemma":[0.002184074,0.0003355252,0.0004533659,0.001129971,0.0006460965,0.002014683,0.002299462,0.0005550914,0.001262551],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004740946,"about_ca_system_score_gemma":0.0006206944,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001251959,"about_ca_topic_score_gemma":0.001117129,"domain_scores_codex":[0.9991292,0.0002260479,0.00003117619,0.0001172476,0.0004218112,0.00007455258],"domain_scores_gemma":[0.9991046,0.0001793447,0.00009141354,0.0004557549,0.0001274126,0.00004134477],"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":[0.0005396719,0.0001649578,0.002096662,0.0002391869,0.0001085005,0.00018475,0.0003149828,0.07404579,0.06071183,0.05628101,0.009235092,0.7960776],"study_design_scores_gemma":[0.0001517321,0.0003760534,0.002129461,0.00002615344,0.00005777894,0.0008529257,0.0001561709,0.8500175,0.04933812,0.0828815,0.01389796,0.0001146403],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02905344,0.0004326414,0.9663457,0.000111851,0.00005672612,0.00005183793,0.0001013267,0.0012901,0.002556305],"genre_scores_gemma":[0.5557638,0.0003593969,0.4393184,0.0001839199,0.0001506076,0.0001193491,0.0003925963,0.0001237294,0.003588159],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002976198,"threshold_uncertainty_score":0.009956419,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01343763792757381,"score_gpt":0.2724570769781449,"score_spread":0.2590194390505711,"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."}}