A Comparative Study of Artificial Intelligence Algorithms Used for an Accurate Image Geometric Dimensions Recognition

The process of distinguishing objects in digital images and recognizing them is a basic process in AI, in order to distinguish the object and determine its features. It has many uses in various engineering and medical fields. There is a great difficulty in recognizing digital images and distinguishing objects in them. This paper discusses some basic artificial intelligence techniques based on the geometric dimensions of the object in the image in order to recognize these objects and distinguish them from the background of the image. Where there is great difficulty in separating the object from its background, this paper presents the most accurate methods of programmatic application using Matlab environment to identify components in the image, its distinction, and its characteristics. The paper provides a comparison between these AI methods and chooses the most accurate one. AI recognition methods use various algorithms including algorithms based on color density and algorithms that depend on geometric dimensions such as Hue moment algorithms, Haralik features algorithms, and Zernike moment algorithms. In this paper these algorithms were applied to a group of images to extract the features of the object and the best one will be chosen with the aid of Matlab. The most accurate recognition process is chosen through building a digital library that contains many pictures and training the program on these images to recognize and distinguish the object in the image.