Textures A Photographic Album For Artists And Designers

By J. Reinhard. 2016

633 citations recorded by the source corpus.

Subfields: Computer Graphics and Computer-Aided Design

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In Computer Graphics and Computer-Aided Design

Citations in the corpus

COCO-Stuff: Thing and Stuff Classes in Context (2016, background): “Early stuff datasets [6, 15, 34, 9] focused on texture classification and had simple images completely covered with a single textured patch.”
Generative Image Modeling Using Spatial LSTMs (2015, methodology): “Figure 3: From top to bottom: A 256 by 256 pixel crop of a texture [2], a sample generated by an MCGSM trained on the full texture [7], and a sample generated by RIDE.”
Recurrent Ladder Networks (2017, neutral): “20 textures from the Brodatz dataset [7].”
Local binary features for texture classification: Taxonomy and experimental study (2017, methodology): “For each performance, such as Brodatz.”
Rotation invariant co-occurrence features based on digital circles and discrete Fourier transform (2014, methodology): “Hardware-rotated digital images have been captured in our lab directly from the original book (Brodatz, 1966) through the imaging system described in Bianconi et al.”
Decentralized Online Learning With Kernels (2017, methodology): “TABLE I COMPARISON OF LIBSVM, BATCHGRAD, L-BFGS, POLK, AND GPPM WITH NULL PENALTY COEFFICIENT c AS WELL AS POSITIVE PENALTY ON THE GAUSSIAN MIXTURES AND BRODATZ [37] DATA SETS”
Approximate Log-Hilbert-Schmidt Distances between Covariance Operators for Image Classification (2016, methodology): “From top to bottom: Fish Recognition [3], Virus Classiﬁcation [16], KTH-TIPS2b Material [5], Texture [4, 8], ETHZ [10], UIUC [20], Tiny-Graz03 [33], and ETH80 [18]. note the color values, and G o,sx,y denotes the Gabor ﬁlter at orientation o and scale s .”
Deep Filter Banks for Texture Recognition, Description, and Segmentation (2015, methodology): “The Brodatz [16] catalogue was used in early works on textures to study the problem of identifying texture instances (e.”
Pairwise Rotation Invariant Co-Occurrence Local Binary Pattern (2014, background): “The Brodatz album [33] is a well-known texture classification benchmark data set.”
Attribute relation learning for zero-shot classification (2014, methodology): “Currently, there are several well-known texture data sets, such as Brodatz [55], MeasTex [56], VisTex [57] and Outex [58].”
From BoW to CNN: Two Decades of Texture Representation for Texture Classification (2018, methodology): “The Brodatz texture database [22], derived the Brodatz Album [23], is the earliest, the most widely used and the most famous texture database.”