41 citations · 370 across the 24 of their papers we have counts for
24 papers
Approximation properties relative to continuous scale space for hybrid discretizations of Gaussian derivative operators
Tony Lindeberg
This paper presents an analysis of properties of two hybrid discretization methods for Gaussian derivatives, based on convolutions with either the normalized sampled Gaussian kerne…
Discrete approximations of Gaussian smoothing and Gaussian derivatives
Tony Lindeberg
This paper develops an in-depth treatment concerning the problem of approximating the Gaussian smoothing and Gaussian derivative computations in scale-space theory for application…
Unified theory for joint covariance properties under geometric image transformations for spatio-temporal receptive fields according to the generalized Gaussian derivative model for visual receptive fields
Tony Lindeberg
The influence of natural image transformations on receptive field responses is crucial for modelling visual operations in computer vision and biological vision. In this regard, cov…
A time-causal and time-recursive analogue of the Gabor transform
Tony Lindeberg
This paper presents a time-causal analogue of the Gabor filter, as well as a both time-causal and time-recursive analogue of the Gabor transform, where the proposed time-causal rep…
Orientation selectivity properties for the affine Gaussian derivative and the affine Gabor models for visual receptive fields
Tony Lindeberg
This paper presents a theoretical analysis of the orientation selectivity of simple and complex cells that can be well modelled by the generalized Gaussian derivative model for vis…
Covariance properties under natural image transformations for the generalized Gaussian derivative model for visual receptive fields
Tony Lindeberg
This paper presents a theory for how geometric image transformations can be handled by a first layer of linear receptive fields, in terms of true covariance properties, which, in t…
A time-causal and time-recursive scale-covariant scale-space representation of temporal signals and past time
Tony Lindeberg
This article presents an overview of a theory for performing temporal smoothing on temporal signals in such a way that: (i) temporally smoothed signals at coarser temporal scales a…
Scale-invariant scale-channel networks: Deep networks that generalise to previously unseen scales
Ylva Jansson, Tony Lindeberg
The ability to handle large scale variations is crucial for many real world visual tasks. A straightforward approach for handling scale in a deep network is to process an image at…
Scale-covariant and scale-invariant Gaussian derivative networks
Tony Lindeberg
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 operatio…
Inability of spatial transformations of CNN feature maps to support invariant recognition
Ylva Jansson, Maksim Maydanskiy, Lukas Finnveden +1
A large number of deep learning architectures use spatial transformations of CNN feature maps or filters to better deal with variability in object appearance caused by natural imag…
Understanding when spatial transformer networks do not support invariance, and what to do about it
Lukas Finnveden, Ylva Jansson, Tony Lindeberg
Spatial transformer networks (STNs) were designed to enable convolutional neural networks (CNNs) to learn invariance to image transformations. STNs were originally proposed to tran…
Exploring the ability of CNNs to generalise to previously unseen scales over wide scale ranges
Ylva Jansson, Tony Lindeberg
The ability to handle large scale variations is crucial for many real world visual tasks. A straightforward approach for handling scale in a deep network is to process an image at…
The problems with using STNs to align CNN feature maps
Lukas Finnveden, Ylva Jansson, Tony Lindeberg
Spatial transformer networks (STNs) were designed to enable CNNs to learn invariance to image transformations. STNs were originally proposed to transform CNN feature maps as well a…
Provably scale-covariant continuous hierarchical networks based on scale-normalized differential expressions coupled in cascade
Tony Lindeberg
This article presents a theory for constructing hierarchical networks in such a way that the networks are guaranteed to be provably scale covariant. We first present a general suff…
Provably scale-covariant networks from oriented quasi quadrature measures in cascade
Tony Lindeberg
This article presents a continuous model for hierarchical networks based on a combination of mathematically derived models of receptive fields and biologically inspired computation…
Dynamic texture recognition using time-causal and time-recursive spatio-temporal receptive fields
Ylva Jansson, Tony Lindeberg
This work presents a first evaluation of using spatio-temporal receptive fields from a recently proposed time-causal spatio-temporal scale-space framework as primitives for video a…
Dense scale selection over space, time and space-time
Tony Lindeberg
Scale selection methods based on local extrema over scale of scale-normalized derivatives have been primarily developed to be applied sparsely --- at image points where the magnitu…
Normative theory of visual receptive fields
Tony Lindeberg
This article gives an overview of a normative computational theory of visual receptive fields, by which idealized functional models of early spatial, spatio-chromatic and spatio-te…
Temporal scale selection in time-causal scale space
Tony Lindeberg
When designing and developing scale selection mechanisms for generating hypotheses about characteristic scales in signals, it is essential that the selected scale levels reflect th…
Discrete approximations of the affine Gaussian derivative model for visual receptive fields
Tony Lindeberg
The affine Gaussian derivative model can in several respects be regarded as a canonical model for receptive fields over a spatial image domain: (i) it can be derived by necessity f…
Separable time-causal and time-recursive spatio-temporal receptive fields
Tony Lindeberg
We present an improved model and theory for time-causal and time-recursive spatio-temporal receptive fields, obtained by a combination of Gaussian receptive fields over the spatial…
Time-causal and time-recursive spatio-temporal receptive fields
Tony Lindeberg
We present an improved model and theory for time-causal and time-recursive spatio-temporal receptive fields, based on a combination of Gaussian receptive fields over the spatial do…
Idealized computational models for auditory receptive fields
Tony Lindeberg, Anders Friberg
This paper presents a theory by which idealized models of auditory receptive fields can be derived in a principled axiomatic manner, from a set of structural properties to enable i…
Invariance of visual operations at the level of receptive fields
Tony Lindeberg
Receptive field profiles registered by cell recordings have shown that mammalian vision has developed receptive fields tuned to different sizes and orientations in the image domain…