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Article
Open AccessDiscrete Approximations of Gaussian Smoothing and Gaussian Derivatives
This paper develops an in-depth treatment concerning the problem of approximating the Gaussian smoothing and the Gaussian derivative computations in scale-space theory for application on discrete data. With cl...
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Article
Open AccessA time-causal and time-recursive scale-covariant scale-space representation of temporal signals and past time
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 are guaranteed to constitute s...
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Article
Open AccessScale-Invariant Scale-Channel Networks: Deep Networks That Generalise to Previously Unseen Scales
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 several scales simultaneously...
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Article
Open AccessScale-Covariant and Scale-Invariant Gaussian Derivative Networks
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. By sharing the...
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Chapter and Conference Paper
Scale-Covariant and Scale-Invariant Gaussian Derivative Networks
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. By sharing the...
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Living Reference Work Entry In depth
Scale Selection
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Reference Work Entry In depth
Scale Selection
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Article
Open AccessProvably Scale-Covariant Continuous Hierarchical Networks Based on Scale-Normalized Differential Expressions Coupled in Cascade
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 sufficiency argument for obtainin...
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Chapter and Conference Paper
Provably Scale-Covariant Networks from Oriented Quasi Quadrature Measures in Cascade
This article presents a continuous model for hierarch...
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Article
Open AccessDynamic Texture Recognition Using Time-Causal and Time-Recursive Spatio-Temporal Receptive Fields
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 analysis. We propose a new fam...
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Article
Open AccessSpatio-Temporal Scale Selection in Video Data
This work presents a theory and methodology for simultaneous detection of local spatial and temporal scales in video data. The underlying idea is that if we process video data by spatio-temporal receptive fiel...
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Article
Open AccessTemporal Scale Selection in Time-Causal Scale Space
When designing and develo** scale selection mechanisms for generating hypotheses about characteristic scales in signals, it is essential that the selected scale levels reflect the extent of the underlying st...
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Chapter and Conference Paper
Spatio-Temporal Scale Selection in Video Data
We present a theory and a method for simultaneous detection of local spatial and temporal scales in video data. The underlying idea is that if we process video data by spatio-temporal receptive fields at multi...
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Chapter and Conference Paper
Dynamic Texture Recognition Using Time-Causal Spatio-Temporal Scale-Space Filters
This work presents an evaluation of using time-causal scale-space filters as primitives for video analysis. For this purpose, we present a new family of video descriptors based on regional statistics of spatio...
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Article
Open AccessTime-Causal and Time-Recursive Spatio-Temporal Receptive Fields
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 domain and first-order integ...
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Article
Open AccessImage Matching Using Generalized Scale-Space Interest Points
The performance of matching and object recognition methods based on interest points depends on both the properties of the underlying interest points and the choice of associated image descriptors. This paper d...
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Chapter and Conference Paper
Separable Time-Causal and Time-Recursive Spatio-Temporal Receptive Fields
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 domain and first-order integ...
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Chapter and Conference Paper
Scale-Space Theory for Auditory Signals
We show how the axiomatic structure of scale-space theory can be applied to the auditory domain and be used for deriving idealized models of auditory receptive fields via scale-space principles. For defining a...
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Reference Work Entry In depth
Scale Selection
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Article
Open AccessA computational theory of visual receptive fields
A receptive field constitutes a region in the visual field where a visual cell or a visual operator responds to visual stimuli. This paper presents a theory for what types of receptive field profiles can be re...
