# List of Figures
> - This folder contains figures, used in [my report](../report/) and [the slides](../slides/).
> - The figures have been generated with [some Python scripts](../src/).

This folder mainly contains PNG figures and GIF animations, obtained with our script on [fractional-direction Hilbert transform](../src/fdHT.py), and other figures obtained with other scripts.
Some are simple plots of functions (e.g. the family $\cos^i(x) \sin^{m-i}(x)$ for $i = 0 \dots m$), used in [my report](../report/).

There is also [this gallery (on my website)](http://perso.crans.org/besson/internship-mva-2016/fig/gallery.html), if needed.

## g-SI steerable convolutions applied to [Stochastic Sparse Processes in 2D](Stochastic_Sparse_Processes_in_2D/)
[This folder](Stochastic_Sparse_Processes_in_2D/) contains many figures illustrating the last experiments we did, using a not-yet open-source GNU Octave / MATLAB implementation of our fractional Laplacian, directional derivatives and elementary blocks $G_{\lambda,\alpha}$.

We applied our gamma-scale-invariant steerable convolution operators to Sparse Stochastic Processes in 2D, mainly of two kinds:

 - Gaussian noise,
 - Compound Poisson noise.

We tried with $\gamma=1$ (to have the elementary block) and $\lambda = 0.3, 0.5, 0.8, 0.9$, and different angles $\alpha = 0, \pi/6, \pi/4, \pi/2, 3\pi/4, \pi$.
Some interpretations and explanations are given in [the report](../report/) (see part 5.5, pages 69-73).

There is also [this other gallery (on my website)](http://perso.crans.org/besson/internship-mva-2016/fig/Stochastic_Sparse_Processes_in_2D/gallery.html), if needed.

---

> [Main directory?](../) | [License?](../LICENSE) | [Some git statistics?](../complete-stats.txt)
> © 2016 [Lilian Besson](http://perso.crans.org/besson/)
[ICO]NameLast modifiedSizeDescription
[PARENTDIR]Parent Directory  -  
[DIR]Stochastic_Sparse_Processes_in_2D/2018-10-04 14:25 -  
[DIR]Stochastic_Sparse_Processes_in_2D__100x100__25-08/2016-08-25 19:32 -  
[DIR]Stochastic_Sparse_Processes_in_2D__100x100__with_Viridis_colormap/2018-10-04 14:25 -  
[IMG]complexplot.png2016-06-24 20:53 225KColor-map for complex-valued fdHT plots, from the script complexplot.py
[IMG]cos2sin1_and_cos1sin2.png2016-07-19 11:53 162KPNG figures for the family of functions cos^k(theta) * sin^{n-k}(theta), k = 0 ... n, from the script plot_cos^k_times_sin^n-k.py
[IMG]cos3sin1_and_cos2sin2_and_cos1sin3.png2016-07-19 11:53 192KPNG figures for the family of functions cos^k(theta) * sin^{n-k}(theta), k = 0 ... n, from the script plot_cos^k_times_sin^n-k.py
[IMG]cos4sin1_and_cos3sin2_and_cos2sin3_and_cos1sin4.png2016-07-19 11:53 227KPNG figures for the family of functions cos^k(theta) * sin^{n-k}(theta), k = 0 ... n, from the script plot_cos^k_times_sin^n-k.py
[IMG]cos5sin1_and_cos4sin2_and_cos3sin3_and_cos2sin4_and_cos1sin5.png2016-07-19 11:53 246KPNG figures for the family of functions cos^k(theta) * sin^{n-k}(theta), k = 0 ... n, from the script plot_cos^k_times_sin^n-k.py
[IMG]cos6sin1_and_cos5sin2_and_cos4sin3_and_cos3sin4_and_cos2sin5_and_cos1sin6.png2016-07-19 11:53 260KPNG figures for the family of functions cos^k(theta) * sin^{n-k}(theta), k = 0 ... n, from the script plot_cos^k_times_sin^n-k.py
[IMG]cos7sin1_and_cos6sin2_and_cos5sin3_and_cos4sin4_and_cos3sin5_and_cos2sin6_and_cos1sin7.png2016-07-19 11:53 253KPNG figures for the family of functions cos^k(theta) * sin^{n-k}(theta), k = 0 ... n, from the script plot_cos^k_times_sin^n-k.py
[IMG]cos8sin1_and_cos7sin2_and_cos6sin3_and_cos5sin4_and_cos4sin5_and_cos3sin6_and_cos2sin7_and_cos1sin8.png2016-07-19 11:53 247KPNG figures for the family of functions cos^k(theta) * sin^{n-k}(theta), k = 0 ... n, from the script plot_cos^k_times_sin^n-k.py
[IMG]fdHT__colored.gif2016-06-23 21:49 2.1MGIF animations of the fdHT, from the script fdHT.py
[IMG]fdHT__colored_1.gif2016-06-23 21:48 2.5MGIF animations of the fdHT, from the script fdHT.py
[IMG]fdHT__colored_1____lena__move_tau.gif2016-06-24 12:57 12MGIF animations of the fdHT, from the script fdHT.py
[IMG]fdHT__colored_1____turn_tau__lena__theta=-pi.gif2016-06-23 21:48 4.0MGIF animations of the fdHT, from the script fdHT.py
[IMG]fdHT__colored_1____turn_theta.gif2016-06-23 21:48 2.9MGIF animations of the fdHT, from the script fdHT.py
[IMG]fdHT__colored_2____lena__move_tau.gif2016-06-24 12:57 12MGIF animations of the fdHT, from the script fdHT.py
[IMG]fdHT__colored_2____lena__turn_theta.gif2016-06-23 21:48 3.6MGIF animations of the fdHT, from the script fdHT.py
[IMG]fdHT__colored_2____turn_tau__lena__theta=-piby2.gif2016-06-23 21:48 3.7MGIF animations of the fdHT, from the script fdHT.py
[IMG]fdHT__colored_3____lena__move_tau.gif2016-06-24 12:58 11MGIF animations of the fdHT, from the script fdHT.py
[IMG]fdHT__colored_3____turn_input__turn_theta.gif2016-06-23 21:49 1.8MGIF animations of the fdHT, from the script fdHT.py
[IMG]fdHT__colored_3____turn_tau__lena__theta=0.gif2016-06-23 21:49 4.0MGIF animations of the fdHT, from the script fdHT.py
[IMG]fdHT__colored_4____lena__move_tau.gif2016-06-24 12:58 12MGIF animations of the fdHT, from the script fdHT.py
[IMG]fdHT__colored_4____turn_tau.gif2016-06-23 21:49 3.1MGIF animations of the fdHT, from the script fdHT.py
[IMG]fdHT__colored_4____turn_tau__lena__theta=piby2.gif2016-06-23 21:49 3.7MGIF animations of the fdHT, from the script fdHT.py
[IMG]fdHT__colored_5____lena__move_tau.gif2016-06-24 12:58 12MGIF animations of the fdHT, from the script fdHT.py
[IMG]fdHT__colored_5____turn_tau__lena.gif2016-06-23 21:49 3.7MGIF animations of the fdHT, from the script fdHT.py
[IMG]fdHT__colored_5____turn_tau__lena__theta=pi.gif2016-06-23 21:49 4.0MGIF animations of the fdHT, from the script fdHT.py
[IMG]fdHT__colored_6____lena__move_tau.gif2016-06-24 12:58 12MGIF animations of the fdHT, from the script fdHT.py
[IMG]fdHT__colored_7____lena__move_tau.gif2016-06-24 12:58 11MGIF animations of the fdHT, from the script fdHT.py
[IMG]fdHT__colored_8____lena__move_tau.gif2016-06-24 12:58 12MGIF animations of the fdHT, from the script fdHT.py
[IMG]fdHT__colored_9____lena__move_tau.gif2016-06-24 12:58 12MGIF animations of the fdHT, from the script fdHT.py
[IMG]fdHT__fig1.png2016-06-24 20:54 219KPNG illustrations of the fdHT, from the script fdHT.py
[IMG]fdHT__fig2.png2016-06-24 20:54 233KPNG illustrations of the fdHT, from the script fdHT.py
[IMG]fdHT__fig3.png2016-06-24 20:54 207KPNG illustrations of the fdHT, from the script fdHT.py
[IMG]fdHT__fig4.png2016-06-24 20:54 218KPNG illustrations of the fdHT, from the script fdHT.py
[IMG]fdHT__fig5.png2016-06-24 20:54 218KPNG illustrations of the fdHT, from the script fdHT.py
[TXT]gallery.html2018-10-04 14:25 23KGalerie photo pour « fig »
[IMG]multi_fdHT__K=5__fig5.png2016-06-24 20:54 504KPNG illustrations of the fdHT, from the script fdHT.py
[IMG]plot_of__r_mapsto_1_by_r_minus_r__0_to_1.png2016-08-17 23:51 16K 
[IMG]tikz_figure_2D_circle_and_angular_sectors.png2016-08-18 00:35 50KPNG figures, generated with TikZ
[IMG]tikz_figure_3D_sphere_and_angular_sectors.png2016-08-18 12:26 84KPNG figures, generated with TikZ
# List of Figures
> - This folder contains figures, used in [my report](../report/) and [the slides](../slides/).
> - The figures have been generated with [some Python scripts](../src/).

This folder mainly contains PNG figures and GIF animations, obtained with our script on [fractional-direction Hilbert transform](../src/fdHT.py), and other figures obtained with other scripts.
Some are simple plots of functions (e.g. the family $\cos^i(x) \sin^{m-i}(x)$ for $i = 0 \dots m$), used in [my report](../report/).

There is also [this gallery (on my website)](http://perso.crans.org/besson/internship-mva-2016/fig/gallery.html), if needed.

## g-SI steerable convolutions applied to [Stochastic Sparse Processes in 2D](Stochastic_Sparse_Processes_in_2D/)
[This folder](Stochastic_Sparse_Processes_in_2D/) contains many figures illustrating the last experiments we did, using a not-yet open-source GNU Octave / MATLAB implementation of our fractional Laplacian, directional derivatives and elementary blocks $G_{\lambda,\alpha}$.

We applied our gamma-scale-invariant steerable convolution operators to Sparse Stochastic Processes in 2D, mainly of two kinds:

 - Gaussian noise,
 - Compound Poisson noise.

We tried with $\gamma=1$ (to have the elementary block) and $\lambda = 0.3, 0.5, 0.8, 0.9$, and different angles $\alpha = 0, \pi/6, \pi/4, \pi/2, 3\pi/4, \pi$.
Some interpretations and explanations are given in [the report](../report/) (see part 5.5, pages 69-73).

There is also [this other gallery (on my website)](http://perso.crans.org/besson/internship-mva-2016/fig/Stochastic_Sparse_Processes_in_2D/gallery.html), if needed.

---

> [Main directory?](../) | [License?](../LICENSE) | [Some git statistics?](../complete-stats.txt)
> © 2016 [Lilian Besson](http://perso.crans.org/besson/)