FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2963505747> ?p ?o ?g. }

• W2963505747 endingPage "113" @default.
• W2963505747 startingPage "100" @default.
• W2963505747 abstract "We propose to recover spectral details from RGB images of known spectral quantization by modeling natural spectra under Gaussian Processes and combining them with the RGB images. Our technique exploits Process Kernels to model the relative smoothness of reflectance spectra, and encourages non-negativity in the resulting signals for better estimation of the reflectance values. The Gaussian Processes are inferred in sets using clusters of spatio-spectrally correlated hyperspectral training patches. Each set is transformed to match the spectral quantization of the test RGB image. We extract overlapping patches from the RGB image and match them to the hyperspectral training patches by spectrally transforming the latter. The RGB patches are encoded over the transformed Gaussian Processes related to those hyperspectral patches and the resulting image is constructed by combining the codes with the original processes. Our approach infers the desired Gaussian Processes under a fully Bayesian model inspired by Beta-Bernoulli Process, for which we also present the inference procedure. A thorough evaluation using three hyperspectral datasets demonstrates the effective extraction of spectral details from RGB images by the proposed technique." @default.
• W2963505747 created "2019-07-30" @default.
• W2963505747 creator A5069697936 @default.
• W2963505747 creator A5089986388 @default.
• W2963505747 date "2020-01-01" @default.
• W2963505747 modified "2023-10-01" @default.
• W2963505747 title "Hyperspectral Recovery from RGB Images using Gaussian Processes" @default.
• W2963505747 cites W1502922572 @default.
• W2963505747 cites W1550575628 @default.
• W2963505747 cites W1890834058 @default.
• W2963505747 cites W1910108985 @default.
• W2963505747 cites W1916874600 @default.
• W2963505747 cites W1975622988 @default.
• W2963505747 cites W1977791453 @default.
• W2963505747 cites W1984699170 @default.
• W2963505747 cites W1987721959 @default.
• W2963505747 cites W1988386267 @default.
• W2963505747 cites W1992195149 @default.
• W2963505747 cites W2002498099 @default.
• W2963505747 cites W2002604566 @default.
• W2963505747 cites W2005876975 @default.
• W2963505747 cites W2010797000 @default.
• W2963505747 cites W2012761844 @default.
• W2963505747 cites W2012946078 @default.
• W2963505747 cites W2014403078 @default.
• W2963505747 cites W2022470997 @default.
• W2963505747 cites W2031007444 @default.
• W2963505747 cites W2031253668 @default.
• W2963505747 cites W2031299022 @default.
• W2963505747 cites W2034114524 @default.
• W2963505747 cites W2043460367 @default.
• W2963505747 cites W2046995795 @default.
• W2963505747 cites W2052468738 @default.
• W2963505747 cites W2063978378 @default.
• W2963505747 cites W2064500837 @default.
• W2963505747 cites W2065579444 @default.
• W2963505747 cites W2082190101 @default.
• W2963505747 cites W2082590892 @default.
• W2963505747 cites W2086099702 @default.
• W2963505747 cites W2087263574 @default.
• W2963505747 cites W2092116045 @default.
• W2963505747 cites W2106383748 @default.
• W2963505747 cites W2106642558 @default.
• W2963505747 cites W2109149397 @default.
• W2963505747 cites W2117111086 @default.
• W2963505747 cites W2125298866 @default.
• W2963505747 cites W2128659236 @default.
• W2963505747 cites W2130835014 @default.
• W2963505747 cites W2135046866 @default.
• W2963505747 cites W2135364872 @default.
• W2963505747 cites W2135619855 @default.
• W2963505747 cites W2143055991 @default.
• W2963505747 cites W2151346360 @default.
• W2963505747 cites W2160547390 @default.
• W2963505747 cites W2162842940 @default.
• W2963505747 cites W2163753106 @default.
• W2963505747 cites W2221899823 @default.
• W2963505747 cites W2466285044 @default.
• W2963505747 cites W2520430674 @default.
• W2963505747 cites W2520844005 @default.
• W2963505747 cites W2740290864 @default.
• W2963505747 cites W2748530166 @default.
• W2963505747 cites W2753685993 @default.
• W2963505747 cites W2761385227 @default.
• W2963505747 cites W2919115771 @default.
• W2963505747 cites W2975229305 @default.
• W2963505747 cites W4206310440 @default.
• W2963505747 cites W8423413 @default.
• W2963505747 doi "https://doi.org/10.1109/tpami.2018.2873729" @default.
• W2963505747 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30295614" @default.
• W2963505747 hasPublicationYear "2020" @default.
• W2963505747 type Work @default.
• W2963505747 sameAs 2963505747 @default.
• W2963505747 citedByCount "49" @default.
• W2963505747 countsByYear W29635057472019 @default.
• W2963505747 countsByYear W29635057472020 @default.
• W2963505747 countsByYear W29635057472021 @default.
• W2963505747 countsByYear W29635057472022 @default.
• W2963505747 countsByYear W29635057472023 @default.
• W2963505747 crossrefType "journal-article" @default.
• W2963505747 hasAuthorship W2963505747A5069697936 @default.
• W2963505747 hasAuthorship W2963505747A5089986388 @default.
• W2963505747 hasBestOaLocation W29635057472 @default.
• W2963505747 hasConcept C121332964 @default.
• W2963505747 hasConcept C153180895 @default.
• W2963505747 hasConcept C154945302 @default.
• W2963505747 hasConcept C159078339 @default.
• W2963505747 hasConcept C163716315 @default.
• W2963505747 hasConcept C31972630 @default.
• W2963505747 hasConcept C33923547 @default.
• W2963505747 hasConcept C41008148 @default.
• W2963505747 hasConcept C61224824 @default.
• W2963505747 hasConcept C61326573 @default.
• W2963505747 hasConcept C62520636 @default.
• W2963505747 hasConcept C82990744 @default.
• W2963505747 hasConceptScore W2963505747C121332964 @default.
• W2963505747 hasConceptScore W2963505747C153180895 @default.
• W2963505747 hasConceptScore W2963505747C154945302 @default.

• next