Non-rigid 3D Model Classification Using 3D Hahn Moment Convolutional Neural Networks

International audienceIn this paper, we propose a new architecture of 3D deep neural network called 3D Hahn Moments Convolutional Neural Network (3D HMCNN) to enhance the classification accuracy and reduce the computational complexity of a 3D pattern recognition system. The proposed architecture is derived by combining the concepts of image Hahn moments and convolutional neural network (CNN), frequently utilized in pattern recognition applications. Indeed, the advantages of the moments concerning their global information coding mechanism even in lower orders, along with the high effectiveness of the CNN, are combined to make up the proposed robust network. The aim of this work is to investigate the classification capabilities of 3D HMCNN on small 3D datasets. The experiment simulations with 3D HMCNN have been performed on the articulated parts of McGill 3D shape Benchmark database and SHREC 2011 database. The obtained results show the significantly high performance in the classification rates of the proposed model and its ability to decrease the computational cost by training low number of features generated by the first 3D moments layer

Non-rigid 3D Model Classification Using 3D Hahn Moment Convolutional Neural Networks

International audienceIn this paper, we propose a new architecture of 3D deep neural network called 3D Hahn Moments Convolutional Neural Network (3D HMCNN) to enhance the classification accuracy and reduce the computational complexity of a 3D pattern recognition system. The proposed architecture is derived by combining the concepts of image Hahn moments and convolutional neural network (CNN), frequently utilized in pattern recognition applications. Indeed, the advantages of the moments concerning their global information coding mechanism even in lower orders, along with the high effectiveness of the CNN, are combined to make up the proposed robust network. The aim of this work is to investigate the classification capabilities of 3D HMCNN on small 3D datasets. The experiment simulations with 3D HMCNN have been performed on the articulated parts of McGill 3D shape Benchmark database and SHREC 2011 database. The obtained results show the significantly high performance in the classification rates of the proposed model and its ability to decrease the computational cost by training low number of features generated by the first 3D moments layer

Efficient Biomedical Signal Security Algorithm for Smart Internet of Medical Things (IoMTs) Applications

Due to the rapid development of information and emerging communication technologies, developing and implementing solutions in the Internet of Medical Things (IoMTs) field have become relevant. This work developed a novel data security algorithm for deployment in emerging wireless biomedical sensor network (WBSN) and IoMTs applications while exchanging electronic patient folders (EPFs) over unsecured communication channels. These EPF data are collected using wireless biomedical sensors implemented in WBSN and IoMTs applications. Our algorithm is designed to ensure a high level of security for confidential patient information and verify the copyrights of bio-signal records included in the EPFs. The proposed scheme involves the use of Hahn’s discrete orthogonal moments for bio-signal feature vector extraction. Next, confidential patient information with the extracted feature vectors is converted into a QR code. The latter is then encrypted based on a proposed two-dimensional version of the modified chaotic logistic map. To demonstrate the feasibility of our scheme in IoMTs, it was implemented on a low-cost hardware board, namely Raspberry Pi, where the quad-core processors of this board are exploited using parallel computing. The conducted numerical experiments showed, on the one hand, that our scheme is highly secure and provides excellent robustness against common signal-processing attacks (noise, filtering, geometric transformations, compression, etc.). On the other hand, the obtained results demonstrated the fast running of our scheme when it is implemented on the Raspberry Pi board based on parallel computing. Furthermore, the results of the conducted comparisons reflect the superiority of our algorithm in terms of robustness when compared to recent bio-signal copyright protection schemes