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This paper presents a hybrid approach between scale-space theory and deep learning, where a deep learning architecture is constructed by coupling parameterized scale-space operations in cascade.
Scale steerable filters for locally scale-invariant convolutional neural networks
Ghosh, R., Gupta, A.K.: · 1906
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Basic theory on normalization of pattern (in case of typical one-dimensional pattern)
Iijima, T.: · 1962
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Scale-space filtering
Witkin, A.P.: · 1983
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The structure of images
Koenderink, J.J.: · 1984
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Uniqueness of the Gaussian kernel for scale-space filtering
Babaud, J., Witkin, A.P., Baudin, M., Duda, R.O.: · 1986
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Representation of local geometry in the visual system
Koenderink, J.J., van Doorn, A.J.: · 1987
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Scale-space for discrete signals
Lindeberg, T.: · 1990
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Generic neighborhood operators
Koenderink, J.J., van Doorn, A.J.: · 1992
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Scale-Space Theory in Computer Vision
Lindeberg, T.: · 1993
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Discrete derivative approximations with scale-space properties: A basis for low-level feature extraction
Lindeberg, T.: · 1993
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Scale-space theory: A basic tool for analysing structures at different scales
Lindeberg, T.: · 1994
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An extended class of scale-invariant and recursive scale-space filters
Pauwels, E.J., Fiddelaers, P., Moons, T., van Gool, L.J.: · 1995
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On the axiomatic foundations of linear scale-space
Lindeberg, T.: · 1996
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Image Structure
Florack, L.M.J.: · 1997
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Feature detection with automatic scale selection
Lindeberg, T.: · 1998
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Edge detection and ridge detection with automatic scale selection
Lindeberg, T.: · 1998
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Feature tracking with automatic selection of spatial scales
Bretzner, L., Lindeberg, T.: · 1998
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Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: · 1998
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Linear scale-space has first been proposed in Japan
Weickert, J., Ishikawa, S., Imiya, A.: · 1999
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Local scale selection for Gaussian based description techniques
Chomat, O., de Verdiere, V., Hall, D., Crowley, J.: · 2000
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Recognition without correspondence using multidimensional receptive field histograms
Schiele, B., Crowley, J.: · 2000
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Spatial and temporal receptive fields of geniculate and cortical cells and directional selectivity
Valois, R.L.D., Cottaris, N.P., Mahon, L.E., Elfer, S.D., Wilson, J.A.: · 2000
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Front-End Vision and Multi-Scale Image Analysis
ter Haar Romeny, B.: · 2003
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Exploring the ability of CNNs to generalise to previously unseen scales over wide scale ranges
Jansson, Y., Lindeberg, T.: · 2004
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Scale and affine invariant interest point detectors
Mikolajczyk, K., Schmid, C.: · 2004
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Distinctive image features from scale-invariant keypoints
Lowe, D.G.: · 2004
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Object recognition using composed receptive field histograms of higher dimensionality
Linde, O., Lindeberg, T.: · 2004
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Local descriptors for spatio-temporal recognition
Laptev, I., Lindeberg, T.: · 2004
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Understanding when spatial transformer networks do not support invariance, and what to do about it
Finnveden, L., Jansson, Y., Lindeberg, T.: · 2004
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On the axioms of scale space theory
Duits, R., Florack, L., de Graaf, J., ter Haar Romeny, B.: · 2004
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The monogenic scale-space: A unifying approach to phase-based image processing in scale-space
Felsberg, M., Sommer, G.: · 2004
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Continuous neural networks
Roux, N.L., Bengio, Y.: · 2007
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Speeded up robust features (SURF)
Bay, H., Ess, A., Tuytelaars, T., van Gool, L.: · 2008
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A Survey on Local Invariant Features. Volume 3(3) of Foundations and Trends in Computer Graphics and Vision
Tuytelaars, T., Mikolajczyk, K.: · 2008
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Maximum membership scale selection
Loog, M., Li, Y., Tax, D.M.J.: · 2009
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Generalized Gaussian scale-space axiomatics comprising linear scale-space, affine scale-space and spatio-temporal scale-space
Lindeberg, T.: · 2011
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Composed complex-cue histograms: An investigation of the information content in receptive field based image descriptors for object recognition
Linde, O., Lindeberg, T.: · 2012
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Jet-based local image descriptors
Larsen, A.B.L., Darkner, S., Dahl, A.L., Pedersen, K.S.: · 2012
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Scale selection for supervised image segmentation
Li, Y., Tax, D.M.J., Loog, M.: · 2012
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Generalized axiomatic scale-space theory
Lindeberg, T.: · 2013
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Warped convolutions: Efficient invariance to spatial transformations
Henriques, J.F., Vedaldi, A.: · 2017
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Automatic differentiation in PyTorch
Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., Lerer, A.: · 2017
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An analysis of scale invariance in object detection — SNIP
Singh, B., Davis, L.S.: · 2018
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Scale equivariance in CNNs with vector fields
Marcos, D., Kellenberger, B., Lobry, S., Tuia, D.: · 2018
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ESPNet: Efficient spatial pyramid of dilated convolutions for semantic segmentation
Mehta, S., Rastegari, M., Caspi, A., Shapiro, L., Hajishirzi, H.: · 2018
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Gabor convolutional networks
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OverFeat: Integrated recognition, localization and detection using convolutional networks
Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., LeCun, Y.: · 2013
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A computational theory of visual receptive fields
Lindeberg, T.: · 2013
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Bruna, J., Mallat, S.: · 2013
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Rotation, scaling and deformation invariant scattering for texture discrimination
Sifre, L., Mallat, S.: · 2013
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Invariance of visual operations at the level of receptive fields
Lindeberg, T.: · 2013
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Scale-invariant convolutional neural networks
Xu, Y., Xiao, T., Zhang, J., Yang, K., Zhang, Z.: · 2014
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Wang, S., Suo, S., Ma, W.C., Pokrovsky, A., Urtasun, R.: · 2018
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Dense scale selection over space, time and space-time
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Deep scale-spaces: Equivariance over scale
Worrall, D., Welling, M.: · 2019
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Provably scale-covariant networks from oriented quasi quadrature measures in cascade
Lindeberg, T.: · 2019
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Scale-aware trident networks for object detection
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ELASTIC: Improving CNNs with dynamic scaling policies
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Drop an octave: Reducing spatial redundancy in convolutional neural networks with octave convolution
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Blurring the line between structure and learning to optimize and adapt receptive fields
Shelhamer, E., Wang, D., Darrell, T.: · 2019
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PointConv: Deep convolutional networks on 3D point clouds
Wu, W., Qi, Z., Fuxin, L.: · 2019
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Provably scale-covariant continuous hierarchical networks based on scale-normalized differential expressions coupled in cascade
Lindeberg, T.: · 2020
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MNIST Large Scale dataset
Jansson, Y., Lindeberg, T.: · 2020
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Scale-equivariant steerable networks
Sosnovik, I., Szmaja, M., Smeulders, A.: · 2020
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B-spline CNNs on Lie groups
Bekkers, E.J.: · 2020
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Inability of spatial transformations of CNN feature maps to support invariant recognition
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From discrete to continuous convolution layers
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Deep neural networks motivated by partial differential equations
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Scale-covariant and scale-invariant Gaussian derivative networks
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Exploring the ability of CNNs to generalise to previously unseen scales over wide scale ranges
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Scale normalized image pyramids with AutoFocus for object detection
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Normative theory of visual receptive fields
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Equivariant deep learning via morphological and linear scale space PDEs on the space of positions and orientations
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Scale selection
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