Multi-Generator Discriminator Network Using Texture-Edge Information

In the proposed paper,a parallel structure type Generative Adversarial Network(GAN)using edge and texture information is proposed.In the existing GAN-based model,many learning iterations had to be given to obtaining an output that was somewhat close to the original data,and noise and distortion occurred in the output image even when learning was performed.To solve this problem,the proposed model consists of two generators and three discriminators to propose a network in the form of a parallel structure.In the network,each edge information and texture information were received as inputs,learning was performed,and each character was combined and outputted through the Combine Discriminator.Through this,edge information and distortion of the output image were improved even with fewer iterations than DCGAN,which is the existing GAN-based model.As a result of learning on the network of the proposed model,a clear image with improved contour and distortion of objects in the image was output from about 50,000 iterations.

Multi-agent reinforcement learning for edge information sharing in vehicular networks

To guarantee the heterogeneous delay requirements of the diverse vehicular services,it is necessary to design a full cooperative policy for both Vehicle to Infrastructure(V2I)and Vehicle to Vehicle(V2V)links.This paper investigates the reduction of the delay in edge information sharing for V2V links while satisfying the delay requirements of the V2I links.Specifically,a mean delay minimization problem and a maximum individual delay minimization problem are formulated to improve the global network performance and ensure the fairness of a single user,respectively.A multi-agent reinforcement learning framework is designed to solve these two problems,where a new reward function is proposed to evaluate the utilities of the two optimization objectives in a unified framework.Thereafter,a proximal policy optimization approach is proposed to enable each V2V user to learn its policy using the shared global network reward.The effectiveness of the proposed approach is finally validated by comparing the obtained results with those of the other baseline approaches through extensive simulation experiments.

Moving Shadow Detection and Removal for Traffic Sequences

Segmentation of moving objects in a video sequence is a basic task for application of computer vision. However, shadows extracted along with the objects can result in large errors in object localization and recognition. In this paper, we propose a method of moving shadow detection based on edge information, which can effectively detect the cast shadow of a moving vehicle in a traffic scene. Having confirmed shadows existing in a figure, we execute the shadow removal algorithm proposed in this paper to segment the shadow from the foreground. The shadow eliminating algorithm removes the boundary of the cast shadow and preserves object edges firstly; secondly, it reconstructs coarse object shapes based on the edge information of objects; and finally, it extracts the cast shadow by subtracting the moving object from the change detection mask and performs further processing. The proposed method has been further tested on images taken under different shadow orientations, vehicle colors and vehicle sizes, and the results have revealed that shadows can be successfully eliminated and thus good video segmentation can be obtained.

Optimized Deep Learning Model for Fire Semantic Segmentation

Recent convolutional neural networks(CNNs)based deep learning has significantly promoted fire detection.Existing fire detection methods can efficiently recognize and locate the fire.However,the accurate flame boundary and shape information is hard to obtain by them,which makes it difficult to conduct automated fire region analysis,prediction,and early warning.To this end,we propose a fire semantic segmentation method based on Global Position Guidance(GPG)and Multi-path explicit Edge information Interaction(MEI).Specifically,to solve the problem of local segmentation errors in low-level feature space,a top-down global position guidance module is used to restrain the offset of low-level features.Besides,an MEI module is proposed to explicitly extract and utilize the edge information to refine the coarse fire segmentation results.We compare the proposed method with existing advanced semantic segmentation and salient object detection methods.Experimental results demonstrate that the proposed method achieves 94.1%,93.6%,94.6%,95.3%,and 95.9%Intersection over Union(IoU)on five test sets respectively which outperforms the suboptimal method by a large margin.In addition,in terms of accuracy,our approach also achieves the best score.

Deep reinforcement learning based worker selection for distributed machine learning enhanced edge intelligence in internet of vehicles

Nowadays,Edge Information System(EIS)has received a lot of attentions.In EIS,Distributed Machine Learning(DML),which requires fewer computing resources,can implement many artificial intelligent applications efficiently.However,due to the dynamical network topology and the fluctuating transmission quality at the edge,work node selection affects the performance of DML a lot.In this paper,we focus on the Internet of Vehicles(IoV),one of the typical scenarios of EIS,and consider the DML-based High Definition(HD)mapping and intelligent driving decision model as the example.The worker selection problem is modeled as a Markov Decision Process(MDP),maximizing the DML model aggregate performance related to the timeliness of the local model,the transmission quality of model parameters uploading,and the effective sensing area of the worker.A Deep Reinforcement Learning(DRL)based solution is proposed,called the Worker Selection based on Policy Gradient(PG-WS)algorithm.The policy mapping from the system state to the worker selection action is represented by a deep neural network.The episodic simulations are built and the REINFORCE algorithm with baseline is used to train the policy network.Results show that the proposed PG-WS algorithm outperforms other comparation methods.

Enlarging Image by Constrained Least Square Approach with Shape Preserving

From a visual point of view, the shape of an image is mainly determined by the edges. Conventional polynomial interpolation of image enlarging methods would produce blurred edges, while edge-directed interpolation based methods would cause distortion in the non-edge areas. A new method for image enlarging is presented. The image is enlarged in two steps. In the first step, a fitting surface is constructed to interpolate the image data. To remove the zigzagging artifact for each pixel, a fitting patch is constructed using edge information as constraints. The combination of all the patches forms the fitting surface which has the shape suggested by image data. Each point on the fitting surface can be regarded as a sampling point taken from a unit square domain, which means that when the fitting surface is used to enlarge the image, each sampling domain of the enlarged pixels is also a unit square, causing the enlarged image to lose some details. To make the enlarged image keep the details as many as possible, the sampling domain of the enlarged pixels should be less than a unit square. Then, in the second step, using the points taken from the fitting surface, new pixels are computed using constrained optimization technique to form the enlarged image, and the size of the sampling domain of the enlarged pixels is inversely proportional to the size of the enlarged image. The image enlarged by the new method has a quadratic polynomial precision. Comparison results show that the new method produces resized image with better quality.