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Research in texture recognition often concentrates on the problem of material recognition in uncluttered conditions, an assumption rarely met by applications.
Textons, the fundamental elements in preattentive vision and perception of textures
B. Julesz and J. R. Bergen · 1983
Earlier work this paper cites.
Textural features corresponding to textural properties
M. Amadasun and R. King · 1989
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Unsupervised texture segmentation using gabor filters
A. Jain and F. Farrokhnia · 1991
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Unsupervised texture segmentation using markov random field models
B. Manjunath and R. Chellappa · 1991
Earlier work this paper cites.
Shape from texture for smooth curved surfaces in perspective projection
J. Gårding · 1992
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Reflectance and texture of real world surfaces
K. J. Dana, B. van Ginneken, S. K. Nayar, and J. J. Koenderink · 1999
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Texture synthesis by non-parametric sampling
A. Efros and T. Leung · 1999
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Object recognition from local scale-invariant features
D. G. Lowe · 1999
Earlier work this paper cites.
Fast texture synthesis using tree-structured vector quantization
L. Wei and M. Levoy · 2000
Earlier work this paper cites.
On seeing stuff: The perception of materials by humans and machines
E. H. Adelson · 2001
Earlier work this paper cites.
Shape from texture and integrability
D. Forsyth · 2001
Earlier work this paper cites.
Representing and recognizing the visual appearance of materials using three-dimensional textons
T. Leung and J. Malik · 2001
Earlier work this paper cites.
Texture classification: Are filter banks necessary?
M. Varma and A. Zisserman · 2003
Earlier work this paper cites.
On the significance of real-world conditions for material classification
E. Hayman, B. Caputo, M. Fritz, and J.-O. Eklundh · 2004
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Class-specific material categorisation
B. Caputo, E. Hayman, and P. Mallikarjuna · 2005
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The PASCAL visual obiect classes challenge 2007 (VOC2007) results
M. Everingham, A. Zisserman, C. Williams, and L. V. Gool · 2007
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Learning visual attributes
V. Ferrari and A. Zisserman · 2007
Cited alongside, same era.
Recognizing indoor scenes
A. Quattoni and A. Torralba · 2009
Cited alongside, same era.
Material perceprion: What can you see in a brief glance?
L. Sharan, R. Rosenholtz, and E. H. Adelson · 2009
Cited alongside, same era.
Graph cut based inference with co-occurrence statistics
L. Ladicky, C. Russell, P. Kohli, and P. Torr · 2010
Cited alongside, same era.
What, where and how many? combining object detectors and crfs
L. Ladicky, P. Sturgess, K. Alahari, C. Russell, and P. Torr · 2010
Cited alongside, same era.
Improving the fisher kernel for large-scale image classification
F. Perronnin, J. Sánchez, and T. Mensink · 2010
Cited alongside, same era.
Return of the devil in the details: Delving deep into convolutional nets
K. Chatfield, K. Simonyan, A. Vedaldi, and A. Zisserman · 2014
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Describing textures in the wild
M. Cimpoi, S. Maji, I. Kokkinos, S. Mohamed, , and A. Vedaldi · 2014
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Rich feature hierarchies for accurate object detection and semantic segmentation
R. B. Girshick, J. Donahue, T. Darrell, and J. Malik · 2014
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Multi-scale orderless pooling of deep convolutional activation features
Y. Gong, L. Wang, R. Guo, and S. Lazebnik · 2014
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Simultaneous detection and segmentation
B. Hariharan, P. Arbeláez, R. Girshick, and J. Malik · 2014
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Spatial pyramid pooling in deep convolutional networks for visual recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2014
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J. Wang, J. Yang, K. Yu, F. Lv, T. Huang, and Y. Gong · 2010
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P. Welinder, S. Branson, T. Mita, C. Wah, and F. Schroff · 2010
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A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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Cnn features off-the-shelf: An astounding baseline for recognition
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Very deep convolutional networks for large-scale image recognition
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Fast features invariant to rotation and scale of texture
M. Sulc and J. Matas · 2014
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Cnn: Single-label to multi-label
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