Object recognition and detection by shape and color pattern recognition utilizing artificial neural networks

• Main Authors: Cruz, Jerome Paul N., Dimaala, Ma. Lourdes, Francisco, Laurene Gaile L., Franco, Erica Joanna S., Bandala, Argel A., Dadios, Elmer P.
• Format: text
• Published: Animo Repository 2013
• Subjects: Neural networks (Computer science); Pattern recognition systems; Electrical and Computer Engineering
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/1147; https://animorepository.dlsu.edu.ph/context/faculty_research/article/2146/type/native/viewcontent
• Institution: De La Salle University

A robust and accurate object recognition tool is presented in this paper. The paper introduced the use of Artificial Neural Networks in evaluating a frame shot of the target image. The system utilizes three major steps in object recognition, namely image processing, ANN processing and interpretation. In image processing stage a frame shot or an image go through a process of extracting numerical values of object's shape and object's color. These values are then fed to the Artificial Neural Network stage, wherein the recognition of the object is done. Since the output of the ANN stage is in numerical form the third process is indispensable for human understanding. This stage simply converts a given value to its equivalent linguistic term. All three components are integrated in an interface for ease of use. Upon the conclusion of the system's development, experimentation and testing procedures are initiated. The study proved that the optimum lighting condition opted for the system is at 674 lumens with an accuracy of 99.99996072%. Another finding that the paper presented is that the optimum distance for recognition is at 40cm with an accuracy of 99.99996072%. Lastly the system contains a very high tolerance in the variations in the objects position or orientation, with the optimum accuracy at upward position with 99.99940181% accuracy rate. © 2013 IEEE.