Intro: quantifying images & some glossary
Under the name "Computer vision" (CV) goes that set of techniques which allow us to manipulate and extract numerical data, and higher-level information, from an image, via a computer. This section will explain the basics of the field and then deal into more details, outlining the concepts that CV tackles. Nowadays, there is a division in the field in regards to "traditional" CV (the set of techniques which is not based on the use of Neural Networks/Deep Learning) and more state-of-the-art methods based on ANNs. We will touch on both sides.
Computer Vision is very interesting - it's about writing code to manipulate and use images and it allows for many applications. Have a read at the two blogs in the references for a bit on the relation between Computer Vision and Machine Learning/Deep Learning.
For the code here in this project, we will use OpenCV through its Python bindings. For the part regarding ANNs, we will make use of TensorFlow and Keras.
The glossary below is meant to contain basic definitions used all over the chapter.
An image with two peaks in the intensity histogram.
Is an image with pixels which can only take values 0 or 1.
The intensity is the pixel value and is defined for grayscale images, where it is an 8-bit integer, hence taking values in the range [0:255], where 0 is black and 255 is white. It is sometimes referred to as brightness.
For coloured images, colour is given as a vector of 3 components of intensity. For example in the RGB values are the intensities for red, green and blue.
Is the difference between maximum and minimum pixel intensities.
A part of image selected for a purpose, like cropping around it or cutting it out.
Some notes on the formalism used are written in this page:
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