Object Recognition and Classification

Abstract

Object recognition and classification is a common problem facing computers. There are many shortcomings in proper identification of an object when it comes to computer algorithms. A very common process used to deal with classification problems is neural networks. Neural networks are modelled after the human brain and the neuron _rings that occur when an individual looks at an image and identifies the objects in it. In this work we propose a probabilistic neural network that takes into account the regional properties of an image of either an ant or an egg as determined by edge segmentation and an extraction of geometric features specific to the object. To do this the algorithm calculates the regional properties of a black and white representation of the object and then gives these properties to the probabilistic neural network which calculates the probability of the object being an ant or an egg.