13 Result(s)
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Article
Tony Lindeberg
Signal, Image and Speech Processing
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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 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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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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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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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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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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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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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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Open AccessScale Selection Properties of Generalized Scale-Space Interest Point Detectors
Scale-invariant interest points have found several highly successful applications in computer vision, in particular for image-based matching and recognition.
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Article
Generalized Gaussian Scale-Space Axiomatics Comprising Linear Scale-Space, Affine Scale-Space and Spatio-Temporal Scale-Space
This paper describes a generalized axiomatic scale-space theory that makes it possible to derive the notions of linear scale-space, affine Gaussian scale-space and linear spatio-temporal scale-space using a si...
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Article
Discrete derivative approximations with scale-space properties: A basis for low-level feature extraction
This article shows how discrete derivative approximations can be defined so thatscale-space properties hold exactly also in the discrete domain. Starting from a set of natural requirements on the first processing...
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Article
Scale-space behaviour of local extrema and blobs
Elementary techniques from real analysis, and singularity theory are applied to derive analytical results for the behaviour in scale-space of critical points and related entities. The main results of the treat...
