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We propose a new architecture for difficult image processing operations, such as natural edge detection or thin object segmentation.
Learning hierarchical features for scene labeling
Farabet, C., Couprie, C., Najman, L., LeCun, Y.: · 1929
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Handwritten digit recognition with a back-propagation network
LeCun, Y., Boser, B.E., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W.E., Jackel, L.D.: · 1989
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Learning low-level vision
Freeman, W.T., Pasztor, E.C., Carmichael, O.T.: · 2000
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Image processing with neural networks -— a review
Egmont-Petersen, M., de Ridder, D., Handels, H.: · 2002
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Ridge-based vessel segmentation in color images of the retina
Staal, J., Abràmoff, M.D., Niemeijer, M., Viergever, M.A., van Ginneken, B.: · 2004
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Region filling and object removal by exemplar-based image inpainting
Criminisi, A., Pérez, P., Toyama, K.: · 2004
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Learning a similarity metric discriminatively, with application to face verification
Chopra, S., Hadsell, R., LeCun, Y.: · 2005
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Supervised learning of image restoration with convolutional networks
Jain, V., Murray, J.F., Roth, F., Turaga, S.C., Zhigulin, V.P., Briggman, K.L., Helmstaedter, M., Denk, W., Seung, H.S.: · 2007
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Natural image denoising with convolutional networks
Jain, V., Seung, H.S.: · 2008
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Image restoration by sparse 3d transform-domain collaborative filtering
Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: · 2008
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VLFeat: An open and portable library of computer vision algorithms
Vedaldi, A., Fulkerson, B.: · 2008
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Learning to detect roads in high-resolution aerial images
Mnih, V., Hinton, G.E.: · 2010
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Modeling pixel means and covariances using factorized third-order boltzmann machines
Ranzato, M., Hinton, G.E.: · 2010
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Contour detection and hierarchical image segmentation
Arbeláez, P., Maire, M., Fowlkes, C., Malik, J.: · 2011
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Indoor scene segmentation using a structured light sensor
Silberman, N., Fergus, R.: · 2011
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., Hinton, G.: · 2012
Cited alongside, same era.
Improving neural networks by preventing co-adaptation of feature detectors
Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: · 2012
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Visualizing and Understanding Convolutional Networks
Zeiler, M.D., Fergus, R.: · 2012
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Discriminatively trained sparse code gradients for contour detection
Xiaofeng, R., Bo, L.: · 2012
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Structured forests for fast edge detection
Dollár, P., Zitnick, C.L.: · 2013
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Boundary detection benchmarking: Beyond f-measures
Hou, X., Yuille, A., Koch, C.: · 2013
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Supervised feature learning for curvilinear structure segmentation
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Deep neural networks segment neuronal membranes in electron microscopy images
Ciresan, D.C., Giusti, A., Gambardella, L.M., Schmidhuber, J.: · 2012
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Learning object-class segmentation with convolutional neural networks
Schulz, H., Behnke, S.: · 2012
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Image denoising: Can plain neural networks compete with bm3d?
Burger, H.C., Schuler, C.J., Harmeling, S.: · 2012
Cited alongside, same era.
Online supplementary material for the article “ N 4 N^{4} -Fields: Neural Network Nearest Neighbor fields for image transforms”
Ganin, Y., Lempitsky, V.:
Cited in the paper.
Becker, C.J., Rigamonti, R., Lepetit, V., Fua, P.: · 2013
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Visual boundary prediction: A deep neural prediction network and quality dissection
Kivinen, J.J., Williams, C.K.I., Heess, N.: · 2014
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OverFeat : Integrated Recognition , Localization and Detection using Convolutional Networks arXiv : 1312 . 6229v3 [ cs . CV ] 14 Jan 2014
Sermanet, P., Eigen, D.: · 2014
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Crisp boundary detection using pointwise mutual information
Isola, P., Zoran, D., Krishnan, D., Adelson, E.H.: · 2014
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