In this article we will see step by step how to create and use a convolutional neural network (CNN) to classify images.
We will discuss in this post a kind of filters widely used by all images software (such as Photoshop or Gimp). In fact and to go further (without “sploiling” the following posts either) this convolution principle will also be widely used by neural networks (Deep Learning) … but we will see that later. First of all, let’s focus on the principle of these convolution filters.
We will see in this article the principles of erosion and dilation of images which are widely used especially during the restoration of poor quality images.
We will see in this article how to perform some basic transformations on images with scikit-image such as rotating, and changing image scale and size.
In this article (which is the 3rd episode of the image processing series) we will see how to use the image histograms we discussed in article 2 to do some basic editing.
In the previous article we saw how our digital images were built and stored. This naturally brings us to the image histograms. Of course we don’t manage an image like we do for a text . Images are in fact just matrix (like a pixel map ), so first of all we need to analyse the image, and to do that we’ll take a look on the pixel histograms.
In this article we will see and especially understand how images are stored in a computer just to make it usable by other softwares. In fact, this post is the first within a series that will allow us to approach image processing in general but also subsequently the place of Artificial Intelligence and especially Deep Learning in this discipline which is part of a set known as computer vision.