Recognition of Geometric Shapes in Images

Abstract

This paper proposes finding optimal ways of recognizing geometric shapes (square, circle, rectangle, etc.) of files in different formats. Shapes recognition is a field of artificial intelligence, which includes all representation and decision techniques to automate the process of identifying similarities between objects or phenomena. An application of shapes recognition requires the definition of descriptors and choosing a distance. An application of shapes recognition is done in two phases: learning and recognition. By learning to calculate the set of descriptors of known (reference). In recognition stage, we calculate the same set of descriptors for unknown form and compared with known shape descriptors. The comparison is made through a distance. Recognition is a decision problem if you say unknown form is the same as the reference shape and if you say unknown form is different form the reference. Classification of forms is done in five steps: Reading and image display, image transformation in binary image and get its negative, Discover crossing thresholds between levels of color, Determine the properties of objects and Classification of objects by shape. Are three methods: Chain codes, the conventional Hough transform, method Harris Corner Detection