Image processing (part 5) Morphologic Transformations
by 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.
Image processing (part 4) Image Transformations
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.
Image processing (part 3) Image Thresholding
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.
Speech recognition with Python & Google
In this article, which is above all practical, we will see how with your computer and Python to capture vocal information and transcribe it into text. Want to create your own digital assistant? why not, here’s how to start it anyway …
Image processing (part 2) the histograms
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.
Image processing (part 1) the digital representation
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.
Facial recognition in video with OpenCV
In this post you’ll figure out how it’s easy to make facial recognition inside a video flow with OpenCV.
Data Access / Python / Text / Tutorials
Getting data from a pdf using Python
In this article you will learn how to easily recover data from a pdf file using Python.
Bias & Variance… dilemma or compromise?
As soon as you begin to create machine learning models, you will be faced with the delicate problem of balance in the adjustment of bias and variance. In this article I try to simply explain how understand these two very important concepts.
