

.. _sphx_glr_auto_examples_applications:

Longer examples and demonstrations
----------------------------------



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    <div class="sphx-glr-thumbnails">

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    <div class="sphx-glr-thumbcontainer" tooltip="Scikit-image currently doesn&#x27;t feature a function that allows you to write text onto an image. However, there is a fairly easy workaround using scikit-image&#x27;s optional dependency matplotlib.">

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  .. image:: /auto_examples/applications/images/thumb/sphx_glr_plot_text_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_applications_plot_text.py`

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      <div class="sphx-glr-thumbnail-title">Render text onto an image</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="This computer vision example shows how to detect faces on an image using object detection framework based on machine learning.">

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  .. image:: /auto_examples/applications/images/thumb/sphx_glr_plot_face_detection_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_applications_plot_face_detection.py`

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      <div class="sphx-glr-thumbnail-title">Face detection using a cascade classifier</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="In various image analysis situations, it is useful to think of the pixels of an image, or of a region of an image, as a network or graph, in which each pixel is connected to its neighbors (with or without diagonals). One such situation is finding the geodesic center of an object, which is the point closest to all other points if you are only allowed to travel on the pixels of the object, rather than in a straight line. This point is the one with maximal closeness centrality [1]_ in the network.">

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  .. image:: /auto_examples/applications/images/thumb/sphx_glr_plot_pixel_graphs_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_applications_plot_pixel_graphs.py`

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      <div class="sphx-glr-thumbnail-title">Use pixel graphs to find an object's geodesic center</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="Image comparison is particularly useful when performing image processing tasks such as exposure manipulations, filtering, and restoration.">

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  .. image:: /auto_examples/applications/images/thumb/sphx_glr_plot_image_comparison_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_applications_plot_image_comparison.py`

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      <div class="sphx-glr-thumbnail-title">Visual image comparison</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="Morphological image processing is a collection of non-linear operations related to the shape or morphology of features in an image, such as boundaries, skeletons, etc. In any given technique, we probe an image with a small shape or template called a structuring element, which defines the region of interest or neighborhood around a pixel.">

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  .. image:: /auto_examples/applications/images/thumb/sphx_glr_plot_morphology_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_applications_plot_morphology.py`

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      <div class="sphx-glr-thumbnail-title">Morphological Filtering</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="In this example, we will see how to segment objects from a background. We use the coins image from skimage.data, which shows several coins outlined against a darker background.">

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  .. image:: /auto_examples/applications/images/thumb/sphx_glr_plot_coins_segmentation_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_applications_plot_coins_segmentation.py`

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      <div class="sphx-glr-thumbnail-title">Comparing edge-based and region-based segmentation</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="Thresholding is used to create a binary image from a grayscale image [1]_. It is the simplest way to segment objects from a background.">

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  .. image:: /auto_examples/applications/images/thumb/sphx_glr_plot_thresholding_guide_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_applications_plot_thresholding_guide.py`

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      <div class="sphx-glr-thumbnail-title">Thresholding</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="Haar-like feature descriptors were successfully used to implement the first real-time face detector [1]_. Inspired by this application, we propose an example illustrating the extraction, selection, and classification of Haar-like features to detect faces vs. non-faces.">

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  .. image:: /auto_examples/applications/images/thumb/sphx_glr_plot_haar_extraction_selection_classification_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_applications_plot_haar_extraction_selection_classification.py`

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      <div class="sphx-glr-thumbnail-title">Face classification using Haar-like feature descriptor</div>
    </div>


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    </div>


.. toctree::
   :hidden:

   /auto_examples/applications/plot_text
   /auto_examples/applications/plot_face_detection
   /auto_examples/applications/plot_pixel_graphs
   /auto_examples/applications/plot_image_comparison
   /auto_examples/applications/plot_morphology
   /auto_examples/applications/plot_coins_segmentation
   /auto_examples/applications/plot_thresholding_guide
   /auto_examples/applications/plot_haar_extraction_selection_classification

