3D image recognition using new set of fractional-order Legendre moments and deep neural networks

Abstract

3D Image representation is an important topic in computer vision and pattern recognition. Recently, 3D image analysis by fractional-order orthogonal moments has provided a new research direction, which has prompted researchers to think about efficient and fast classification. In this paper, the authors derived novel sets of fractional-order Legendre moment invariants (FrLMIs), for 3D object description and recognition. Therefore, an analysis of the performance of reconstruction and classification based on fractional-order moment invariants and Deep Neural Networks (DNNs) by changing the number of descriptors was presented. Accordingly, the performance of these proposed fractional-order moments and moment invariants are evaluated through several appropriate experiments, including 3D image reconstruction, region of interest feature extraction, invariance with respect to the geometric transformations and noisy, and 3D Object classification using different fractional parameters. The superiority of the proposed method is verified by comparing the classification percentages obtained by varying the amount of data used during the training process in comparison with the existing methods. The work presented will help to create new neural network architectures that take advantage of the descriptive capacity of 3D fractional-order moments. Finally, these fractional-order moments are very fast and computationally inexpensive which could be useful in many computer vision applications. Based on these characteristics, the proposed FrOLMs and FrLMIs outperformed all existing orthogonal moments.