3D Object Recognition Using Multiple Views, Affine Moment Invariants and Multilayered Perceptron Network

This paper addresses a performance analysis of affine moment invariants for 3D object recognition. Affine moment invariants are commonly used as shape feature for 2D object or pattern recognition. The current study proved that with some adaptation to multiple views technique, affine moments are sufficient to model 3D objects. In addition, the simplicity of moments calculation reduces the processing time for feature extraction, hence increases the system efficiency. In the recognition stage, we proposed to use multilayered perceptron (MLP) network trained by Levenberg-Marquardt algorithm for matching and classification. The proposed method has been tested using two groups of object, polyhedral and free-form objects. The experimental results show that affine moment invariants combined with MLP network attain the best performance in both recognitions, polyhedral and free-form objects. Key-Words: Computer vision, multiple views technique, moment invariants, 3D object recognition, neural networks