A Novel Visual SLAM Based on Multiple Deep Neural Networks

Aircraft Visual Identification by Neural Networks

In the present paper, an efficient method for three dimensional aircraft pattern recognition is introduced. In this method, a set of simple area based features extracted from silhouette of aerial vehicles are used to recognize an aircraft type from its optical or infrared images taken by a CCD camera or a FLIR sensor. These images can be taken from any direction and distance relative to the fly...

Evolutionary Visual Analysis of Deep Neural Networks

Recently, deep learning visualization gained a lot of attentions for understanding deep neural networks. However, there is a missing focus on the visualization of deep model training process. To bridge the gap, in this paper, we firstly define a discriminability metric to evaluate neuron evolution and a density metric to investigate output feature maps. Based on these metrics, a level-ofdetail ...

Visual Servoing from Deep Neural Networks

We present a deep neural network-based method to perform high-precision, robust and real-time 6 DOF visual servoing. The paper describes how to create a dataset simulating various perturbations (occlusions and lighting conditions) from a single real-world image of the scene. A convolutional neural network is fine-tuned using this dataset to estimate the relative pose between two images of the s...

Visual Trajectory Based SLAM

A universal VAD based on jointly trained deep neural networks

In this paper, we propose a joint training approach to voice activity detection (VAD) to address the issue of performance degradation due to unseen noise conditions. Two key techniques are integrated into this deep neural network (DNN) based VAD framework. First, a regression DNN is trained to map the noisy to clean speech features similar to DNN-based speech enhancement. Second, the VAD part t...