Object Recognition using Fuzzy Membership Function Rules
Hunter, Jonathan Edward
Object recognition and learning algorithms are huge areas of robotics research with many different methods in use by various researchers. A common result of using complex recognition methods is the loss of meaning (for humans) in the subsequent processing of the data. When programs incorrectly identify objects, the reason why is often lost in the data analysis. If the researchers can understand what the robot sees, they are better able to develop a system that has limited image understanding. Fuzzy models in object recognition are one of the better methods for achieving such a learning system. Our desire is to develop a system that is quickly and easily trained, a system that can relate the decision of objects through the feature vector (vector of measured characteristics about the object), and a system that is relatively simple in its calculation of results. The research was applied in conjunction with an experiment done by the Psychology Department. This system was applied to recorded videos of tasks done by their subjects. The system proves to be quite effective in object recognition and provides many options for more advanced data processing.