Tesis: Method to classification and recognition of hue in human teeth using image processing implementing techniques of Machine Learning.

In this repository, an empirical comparison of different supervised learning algorithms is made for the automatic recognition of hues in human teeth to help minimize the process of manufacturing dental prostheses.

The methodological For this specific project is divided into 5 stages which will be presented below:

1. Collection of Dataset:In this part of the project the necessary data for the development of the project will be collected, which must contain specific characteristics; the file with the particular data and the name of said data.
2. The extraction of characteristics, which by means of the code that as its name indicates it identifies the main or more relevant characteristics of the data for its later classification.
3. Afterwards, we will proceed to the sampling in which the data will be classified into two groups, the training data and the test data, assigning a percentage for each group and one that the allocation of these percentages can be somewhat subjective, normally these percentages oscillate 20% and 80%.
4. The fourth part of the methodological used for the development of the project is the classification of the data previously processed in the sub groups that are desired for the development of the project, which in this specific case are 5 different groups since they refer to the hues in human teeth.