Write spectra project
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---
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layout: page
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layout: page
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title: Spectra
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title: Spectra
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description: a project with a background image
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description: Application using the morphological hierarchies and LiDAR data
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img: assets/img/spectra.jpg
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img: /assets/img/spectra.png
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importance: 1
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importance: 1
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category: thesis
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category: thesis
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---
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---
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$$
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This application allows interactive filtering of LiDAR data. We can
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E = mc^2 \frac{42}{10^5}
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retrieve structures in the LiDAR data based on the elevation values of
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$$
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the connected components and their shape and size attributes. To guide
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the attribute thresholding we can plot the attribute space representing
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all the shapes and sizes of the structures contained in the LiDAR data.
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Such a plot can be called a shape-size pattern spectrum.
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{class="img-fluid rounded z-depth-1"}
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{.figure-img .img-fluid .rounded
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.z-depth-1}
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{:class="img-fluid rounded z-depth-1"}
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We can use this spectrum to select the attribute thresholds. The current
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application allows us to do this in real time!
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::: {:class="caption"}
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{.figure-img
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:::
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.img-fluid .rounded .z-depth-1}
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In this spectrum, the x-axis represents the area attribute. Selecting
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the right part of the spectrum allows us to filter the largest connected
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components, while selecting the left part allows us to filter the
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smallest connected components. In between we can characterize different
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classes of structures sizes, including cars, trees, buildings. The
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y-axis represents the compactness attribute. The compactness attribute
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is defined as the ratio between the area of the connected component and
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the square of its perimeter. It ranges from 0 for non-compact shapes to
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1 for compact shapes. The top of the spectrum represents compact shapes
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(e.g. circular shapes) while the bottom of the spectrum represents
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linear shapes.
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Selecting thresholds in the attribute space can still be difficult. We
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propose to drive the threshold selection by the example. The application
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allows to select in the LiDAR data structures and to highlight their
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shapes and sizes in the spectrum.
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{.figure-img
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.img-fluid .rounded .z-depth-1 }
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<div class="row">
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In the previous example we showed the use of two attributes, area and
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<div class="col-sm mt-3 mt-md-0">
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compactness. However, there are many more that we can use or even
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{% include figure.html path="assets/img/1.jpg" title="example image" class="img-fluid rounded z-depth-1" %}
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define, depending on the purpose of the application.
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</div>
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<div class="col-sm mt-3 mt-md-0">
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{% include figure.html path="assets/img/3.jpg" title="example image" class="img-fluid rounded z-depth-1" %}
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</div>
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<div class="col-sm mt-3 mt-md-0">
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{% include figure.html path="assets/img/5.jpg" title="example image" class="img-fluid rounded z-depth-1" %}
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</div>
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</div>
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<div class="caption">
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Caption photos easily. On the left, a road goes through a tunnel. Middle, leaves artistically fall in a hipster photoshoot. Right, in another hipster photoshoot, a lumberjack grasps a handful of pine needles.
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</div>
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<div class="row">
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<div class="col-sm mt-3 mt-md-0">
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{% include figure.html path="assets/img/5.jpg" title="example image" class="img-fluid rounded z-depth-1" %}
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</div>
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</div>
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<div class="caption">
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This image can also have a caption. It's like magic.
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</div>
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You can also put regular text between your rows of images.
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Furthermore, the attribute space does not necessarily have to be limited
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Say you wanted to write a little bit about your project before you posted the rest of the images.
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by two dimensions. If for now we are limited to a maximum of 3
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You describe how you toiled, sweated, *bled* for your project, and then... you reveal its glory in the next row of images.
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dimensions for understanding and visualisation (as seen in the 3D
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spectrum bellow), we can look forward to using new visualisation and
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innovative user interfaces to jump into the multi-dimensional attribute
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space exploration!
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<div class="row justify-content-sm-center">
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{.figure-img .img-fluid
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<div class="col-sm-8 mt-3 mt-md-0">
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.rounded .z-depth-1 loop=true autoplay=true}
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{% include figure.html path="assets/img/6.jpg" title="example image" class="img-fluid rounded z-depth-1" %}
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</div>
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<div class="col-sm-4 mt-3 mt-md-0">
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{% include figure.html path="assets/img/11.jpg" title="example image" class="img-fluid rounded z-depth-1" %}
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</div>
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</div>
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<div class="caption">
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You can also have artistically styled 2/3 + 1/3 images, like these.
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</div>
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The underlying data structure for processing LiDAR data are hierarchical
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morphologies, in particular component trees, which allow an efficient
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representation of nested connected components for the computation of
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shape and size attributes as well as efficient filtering.
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The code is simple.
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The application was developed in Python using the [SAP
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Just wrap your images with `<div class="col-sm">` and place them inside `<div class="row">` (read more about the <a href="https://getbootstrap.com/docs/4.4/layout/grid/">Bootstrap Grid</a> system).
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package](/projects/sap/) to build the trees, compute the spectra and
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To make images responsive, add `img-fluid` class to each; for rounded corners and shadows use `rounded` and `z-depth-1` classes.
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filter the data.
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Here's the code for the last row of images above:
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{% raw %}
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```html
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<div class="row justify-content-sm-center">
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<div class="col-sm-8 mt-3 mt-md-0">
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{% include figure.html path="assets/img/6.jpg" title="example image" class="img-fluid rounded z-depth-1" %}
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</div>
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<div class="col-sm-4 mt-3 mt-md-0">
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{% include figure.html path="assets/img/11.jpg" title="example image" class="img-fluid rounded z-depth-1" %}
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</div>
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</div>
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```
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{% endraw %}
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This application was developed as part of my Ph.D. thesis.
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assets/img/spectra.png
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assets/img/spectra.png
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After Width: | Height: | Size: 194 KiB |
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assets/vid/3D_axis2_trans_2x.mp4
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assets/vid/3D_axis2_trans_2x.mp4
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assets/vid/spetra_rennes_area_compactness.mp4
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assets/vid/spetra_rennes_area_compactness.mp4
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assets/vid/spetra_rennes_area_compactness_select.mp4
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assets/vid/spetra_rennes_area_compactness_select.mp4
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