Table of contents added to README file.

README.md

@@ -1,13 +1,29 @@
-# landscape-recognizer
+# Landscape recognizer
 
 Supervised ML classification algorithm implemented through pattern recognition techniques and statistical methods.
 
-
 ![Python](https://img.shields.io/badge/python-3670A0?style=flat&logo=python&logoColor=white)
 ![OpenCV](https://img.shields.io/badge/opencv-%23white.svg?style=flat&logo=opencv&logoColor=white)
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+## Table of contents
+
+* [Landscape recognizer](#landscape-recognizer)
+  * [Table of contents](#table-of-contents)
+  * [Project description](#project-description)
+    * [Constraints](#constraints)
+  * [Algorithm design and implementation](#algorithm-design-and-implementation)
+    * [Feature vector definition](#feature-vector-definition)
+    * [Pattern recognition and learning algorithm](#pattern-recognition-and-learning-algorithm)
+    * [Implementation](#implementation)
+  * [Resulting model](#resulting-model)
+  * [Testing](#testing)
+    * [Testing results](#testing-results)
+  * [Conclusion](#conclusion)
+  * [Author](#author)
+  * [License](#license)
+
 ## Project description
 
 This project is focused on the design, implementation, and testing of a supervised machine learning algorithm aimed at **detecting urban-natural landscapes**. Leveraging pattern recognition techniques and statistical methods, the algorithm goal is to accurately logit classify images based on their color profile features. By harnessing the power of machine learning, this approach offers the potential to automate the identification process, enabling efficient analysis of large-scale datasets.