pyscsp 1.0.3
Scale-Space Toolbox for Python.
License: MIT
Requires Python: <4.0,>=3.7

# pyscsp : Scale-Space Toolbox for Python

Contains the following modules:

## discscsp : Discrete Scale-Space and Scale-Space Derivative Toolbox for Python:

This module comprises:

-- functions for computing spatial scale-space representations by spatial smoothing 
     with the discrete analogue of the Gaussian kernel or other discrete approximations 
	 of the continuous Gaussian kernel, that is used for defining a Gaussian 
	 scale-space representation.

-- functions for computing discrete approximations of Gaussian
	derivatives, based on first convolving the image data with the
	discrete analogue of the Gaussian kernel, and then applying
	small-support central difference operations to the spatial
	smoothed image data.
	
-- functions for computing differential expressions in terms of scale-normalized
    Gaussian derivatives for different purposes in feature detection
    from image data, such as edge detection, interest point detection
    (blob detection or corner detection) and ridge detection, based on
    the above discrete derivative approximations obtained by applying
    small-support central difference operations to spatially smoothed
    image data obtained by convolving the input image with the
    discrete analogue of the Gaussian kernel.
	
For examples of how to apply these functions for computing scale-space
features, please see the enclosed Jupyter notebook 
[discscspdemo.ipynb](https://github.com/tonylindeberg/pyscsp/blob/main/discscspdemo.ipynb).

For more technical descriptions about the respective functions, as well
as explanations of the theoretical properties for different discrete
approximations of the Gaussian kernel, please see the documentation
strings for the respective functions in the source code in 
[discscsp.py](https://github.com/tonylindeberg/pyscsp/blob/main/pyscsp/discscsp.py).

## gaussders: Gaussian Derivative Toolbox for Python

This module comprises:

-- functions for computing discrete approximations of Gaussian
	derivatives, based on convolving the image data with sampled Gaussian derivative
	kernels or integrated Gaussian derivative kernels.

-- functions for computing differential expressions in terms of scale-normalized
    Gaussian derivatives for different purposes in feature detection
    from image data, such as edge detection, interest point detection
    (blob detection or corner detection) and ridge detection, based on
    the above discrete derivative approximations obtained by convolving
    the input image with either sampled Gaussian derivative kernels or
    integrated Gaussian derivative kernels.
	
For examples of how to apply these functions for computing scale-space
features, please see the enclosed Jupyter notebook 
[gaussdersdemo.ipynb](https://github.com/tonylindeberg/pyscsp/blob/main/gaussdersdemo.ipynb).

For more technical descriptions about the respective functions, as well
as explanations of the theoretical properties for different discrete
approximations of the Gaussian kernel, please see the documentation
strings for the respective functions in the source code in
[gaussders.py](https://github.com/tonylindeberg/pyscsp/blob/main/pyscsp/gaussders.py).

## affscsp : Affine Scale-Space and Scale-Space Derivative Toolbox for Python:

This module comprises:

-- functions for computing discrete approximations of affine Gaussian
	kernels and affine Gaussian directional derivative approximation
	masks, which can be used for computing the responses of filter
	banks of  directional derivative responses over different orders of 
	spatial differentiation and over different image orientations.

For examples of how to apply these functions for computing scale-space
features, please see the enclosed Jupyter notebook 
[affscspdemo.ipynb](https://github.com/tonylindeberg/pyscsp/blob/main/affscspdemo.ipynb).

For more technical descriptions about the respective functions, please
see the documentation strings for the respective functions in the source
code in 
[affscsp.py](https://github.com/tonylindeberg/pyscsp/blob/main/pyscsp/affscsp.py).

## torchscsp : Subset of functionalities for use in PyTorch:

This module comprises:

-- functions for generating 1-D discrete approximations of the Gaussian kernel
     for spatial smoothing with separable filtering in PyTorch,
	 
-- discrete derivative approximation masks for computing discrete approximations
     of Gaussian derivatives and Gaussian derivative layers in PyTorch.

-- functions for generating affine Gaussian kernels and scale-normalized discrete
   directional derivative approximation masks, which can be used for computing the responses to
   filter banks of directional derivatives of affine Gaussian kernels in PyTorch.

For more technical descriptions about the respective functions, please
see the documentation strings for the respective functions in the source
code in 
[torchscsp.py](https://github.com/tonylindeberg/pyscsp/blob/main/pyscsp/torchscsp.py).

## Installation:

These modules can be installed using pip.

To install only the discscsp, gaussders and affscsp modules
(without installing the torchscsp module which requires a larger
installation of PyTorch) do:
```bash
pip install pyscsp
```

To install also the torchscsp module, do instead perform the
following command:
```bash
pip install 'pyscsp[torch]'
```
Note, however, that you must then have PyTorch already installed to use
this option. Otherwise, the installation command may generate an
error message.

These modules can also be downloaded directly from GitHub:

```bash
git clone git@github.com:tonylindeberg/pyscsp.git
```


## References:

Lindeberg (1990) "Scale-space for discrete signals", IEEE Transactions on
Pattern Analysis and Machine Intelligence, 12(3): 234--254.
([preprint](http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-58057))

Lindeberg (1993a) "Discrete derivative approximations with scale-space properties: 
A basis for low-level feature detection", Journal of Mathematical Imaging and Vision, 
3(4): 349-376.
([preprint](https://kth.diva-portal.org/smash/record.jsf?pid=diva2%3A473368&dswid=3752))

Lindeberg (1993b) Scale-Space Theory in Computer Vision, Springer.
([Online edition](http://dx.doi.org/10.1007/978-1-4757-6465-9))

Lindeberg (1994) "Scale-space theory: A basic tool for analysing
structures at different scales", Journal of Applied Statistics 21(2):
224-270.
([preprint](http://kth.diva-portal.org/smash/record.jsf?pid=diva2%3A457189&dswid=7387))

Lindeberg and Garding (1997) "Shape-adapted smoothing in estimation 
of 3-D depth cues from affine distortions of local 2-D structure",
Image and Vision Computing 15: 415-434
([preprint](http://kth.diva-portal.org/smash/record.jsf?pid=diva2%3A472972&dswid=2395))

Lindeberg (1998a) "Feature detection with automatic scale selection", 
International Journal of Computer Vision, vol 30(2): 77-116.
([preprint](http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-40224))

Lindeberg (1998b) "Edge detection and ridge detection with automatic scale selection", 
International Journal of Computer Vision, vol 30(2): 117-154.
([preprint](http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-40226))

Lindeberg (2009) "Scale-space". In: B. Wah (Ed.) Wiley Encyclopedia of Computer 
Science and Engineering, John Wiley & Sons, pp. 2495-2504.
([preprint](https://kth.diva-portal.org/smash/record.jsf?pid=diva2%3A441147&dswid=2409))

Lindeberg (2013a) "A computational theory of visual receptive fields", 
Biological Cybernetics, 107(6): 589-635.
([Open Access](https://doi.org/10.1007/s00422-013-0569-z))

Lindeberg (2013b) "Scale selection properties of generalized
scale-space interest point detectors", Journal of Mathematical
Imaging and Vision, 46(2): 177-210.
([Open Access](https://doi.org/10.1007/s10851-012-0378-3))

Lindeberg (2015) "Image matching using generalized scale-space
interest points", Journal of Mathematical Imaging and Vision, 52(1):
3-36.
([Open Access](https://dx.doi.org/10.1007/s10851-014-0541-0))

Lindeberg (2021) "Normative theory of visual receptive fields", 
Heliyon 7(1): e05897: 1-20.
([Open Access](https://doi.org/10.1016/j.heliyon.2021.e05897))

Lindeberg (2022) "Scale-covariant and scale-invariant Gaussian derivative 
networks", Journal of Mathematical Imaging and Vision, 64(3): 223-242.
([Open Access](https://doi.org/10.1007/s10851-021-01057-9))

Lindeberg (2024) "Discrete approximations of Gaussian smoothing and
Gaussian derivatives", Journal of Mathematical Imaging and Vision,
66(5): 759-800.
([Open Access](https://doi.org/10.1007/s10851-024-01196-9))

Lindeberg (2025) "Approximation properties relative to continuous scale space for hybrid discretizations of Gaussian derivative operators", Frontiers in Signal Processing, 4: 144784: 1-12.
([Open Access](https://doi.org/10.3389/frsip.2024.1447841))


## Relations between the scientific papers and concepts in this code:

The paper (Lindeberg 1990) describes the discrete analogue of the
Gaussian kernel used for discrete implementation of Gaussian
smoothing, including its theoretical properties and how it can be
defined by uniqueness from a set of theoretical assumptions
(scale-space axioms) that reflect desirable properties of a
scale-space smoothing operation. This paper also describes some of the
theoretical properties of the sampled Gaussian kernel.

The paper (Lindeberg 1993a) describes how discrete derivative
approximations defined by applying difference operators to a discrete
scale-space representation preserve scale-space properties of discrete
approximations of Gaussian derivatives, provided that the scale-space
smoothing operattion is performed using the discrete analogue of the
Gaussian kernel.

Chapters 3-5 in the book (Lindeberg 1993b) give a more extensive treatment of
discrete scale-space representation defined by convolution with the
discrete analogue of the Gaussian kernel, including scale-space
properties of discrete derivative approximations defined by applying
difference operators to the discrete scale-space representation
defined by convolution with the discrete analogue of the Gaussian
kernel. This treatment also describes theoretical properties of the
sampled Gaussian kernel, the integrated Gaussian kernel and the
linearily integrated Gaussian kernel.

The paper (Lindeberg 1994) gives a comprehensive general overview over the
notion of Gaussian scale-space representation.

Chapter 14 in the book (Lindeberg 1993b) and the paper
(Lindeberg and Garding 1997) describe the notion of affine Gaussian
scale space, with its closedness property under affine image
transformations, referred to as affine covariance or affine equivariance.

The paper (Lindeberg 1998a) describes the blob detector based on the
spatial extrema of the Laplacian operator (N-jet function 'Laplace'), the
interest point detector based on spatial extrema of the determinant of
the Hessian operator (N-jet function 'detHessian') and the corner
detector based on spatial extrema of the rescaled level curve
curvature operator (N-jet function 'Kappa'). This paper also defines
the notion of gamma-normalized scale-space derivatives by multiplying
the regular Gaussian derivative operators by the scale parameter s =
sigma^2 raised to the power of gamma multiplied by the order of
differentiation and divided by two.

The paper (Lindeberg 1998b) describes the differential definition of
edge detection from local directional derivatives of the image
intensity in the gradient direction (N-jet functions 'Lv', 'Lv2Lvv' and
Lv3Lvv') as well as corresponding ridge and valley detectors defined
from directional derivatives in the principal curvature directions (p,
q) of the grey-level landscape (N-jet functions 'Lp', 'Lq', 'Lpp' and
'Lqq').

The paper (Lindeberg 2009) gives a comprehensive overview of basic
components in scale-space theory, and can in this respect serve as a
good first introduction to this area, including demonstrations of how
different types of differential invariants in scale-space (in this
code referred to as N-jet functions) can be used for basic purposes of
detecting image features in image data.

The papers (Lindeberg 2013a) and (Lindeberg 2021) demonstrate how
the spatial component of the receptive fields of simple cells in
the primary visual cortex can be well modelled by directional
derivatives of affine Gaussian kernels. In the affscsp module, we
provide functions for generating such kernels corresponding to
directional derivatives of affine Gaussian kernels and for computing
the effect of convolving images with such kernels.

The papers (Lindeberg 2013b, Lindeberg 2015) give a more modern treatment of some of
the concepts described in (Lindeberg 1998a), regarding the use of
spatial extrema of the Laplacian operator (N-jet function 'Laplace'),
the determinant of the Hessian operator (N-jet function 'detHessian')
and the rescaled level curve curvature operator (N-jet function 'Kappa')
for interest point detection.

The paper (Lindeberg 2022) defines the notion of a Gaussian derivative
layer, as a linear combination of scale-normalized Gaussian derivative
responses, as a basic concept for defining provably scale-covariant
and scale-invariant deep networks.

The paper (Lindeberg 2024) gives an in-depth treatment of different
ways of approximating the Gaussian smoothing operation and the
Gaussian derivative operators that underlie the computation of
scale-space features. In this respect, this paper provides both
the theoretical foundations and quantitative performance
characterizations for many of the implementations in
the pyscsp package.

The paper (Lindeberg 2025) extends the treatment of discrete
approximations of Gaussian derivative operators to a characterization
of properties of the hybrid discretization methods, based on
combinations of a first stage of spatial smoothing, with either the
normalized sampled Gaussian kernel or the integrated Gaussian kernel,
followed by central differences. These discretization methods are
computationally more efficient in situations when multiple derivatives
of different orders are to be computed at the same scale level,
compared to explicit convolutions with either sampled Gaussian
derivative kernels or integrated Gaussian derivative kernels. 

## Remarks: 

To avoid possible misunderstandings, this pyscsp package does
not contain the full implementations needed to reproduce the methods
in the above papers, only a subset of basic functionalities regarding
the first layer of computations on the image data.

The original implementations for most of the above papers have been
performed in C or Matlab. Only for the papers (Lindeberg 2022),
(Lindeberg 2024) and (Lindeberg 2025), 
the experimental work has been based on Python implementations.

The more general set of references listed here is, however, provided
to point to a wider context, in which the basic functions in the
pyscsp package can be used.