Computerized registration between maxillofacial cone-beam computed tomography (CT) images and a scanned dental model is an essential prerequisite for surgical planning for dental implants or orthognathic surgery. We propose a novel method that perfor...
We introduce Post-DAE, a post-processing method based on denoising autoencoders (DAE) to improve the anatomical plausibility of arbitrary biomedical image segmentation algorithms. Some of the most popular segmentation methods (e.g. based on convoluti...
IEEE transactions on neural networks and learning systems
Nov 30, 2020
From the medical field to agriculture, from energy to transportation, every industry is going through a revolution by embracing artificial intelligence (AI); nevertheless, AI is still in its infancy. Inspired by the evolution of the human brain, this...
IEEE transactions on neural networks and learning systems
Nov 30, 2020
Recently, the dynamical behaviors of coupled neural networks (CNNs) with and without reaction-diffusion terms have been widely researched due to their successful applications in different fields. This article introduces some important and interesting...
IEEE transactions on neural networks and learning systems
Nov 30, 2020
We show that a neural network whose output is obtained as the difference of the outputs of two feedforward networks with exponential activation function in the hidden layer and logarithmic activation function in the output node, referred to as log-su...
IEEE transactions on neural networks and learning systems
Nov 30, 2020
The aim of this article is to investigate the trajectory tracking problem of systems with uncertain models and state restrictions using differential neural networks (DNNs). The adaptive control design considers the design of a nonparametric identifie...
IEEE transactions on neural networks and learning systems
Nov 30, 2020
Recently, applications of complex-valued neural networks (CVNNs) to real-valued classification problems have attracted significant attention. However, most existing CVNNs are black-box models with poor explanation performance. This study extends the ...
IEEE transactions on neural networks and learning systems
Nov 30, 2020
In the literature, the effects of switching with average dwell time (ADT), Markovian switching, and intermittent coupling on stability and synchronization of dynamic systems have been extensively investigated. However, all of them are considered sepa...
IEEE transactions on neural networks and learning systems
Nov 30, 2020
In this article, the finite-time H state estimation problem is addressed for a class of discrete-time neural networks with semi-Markovian jump parameters and time-varying delays. The focus is mainly on the design of a state estimator such that the co...
IEEE transactions on neural networks and learning systems
Nov 30, 2020
This article proposes an unsupervised address event representation (AER) object recognition approach. The proposed approach consists of a novel multiscale spatio-temporal feature (MuST) representation of input AER events and a spiking neural network ...
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