基于目标检测及边缘支持的鱼类图像分割方法

对图像中的鱼类目标进行分割是提取鱼类生物学信息的关键步骤。针对现有方法对养殖条件下的鱼类图像分割精度较低的问题,提出了基于目标检测及边缘支持的鱼类图像分割方法。首先,设计了基于目标检测的完整轮廓提取方法,将具有完整轮廓的鱼类目标从图像中提取出来作为分割阶段的输入,使得整幅图像的分割问题转化为局部区域内的分割问题;然后,搭建Canny边缘支持的深度学习分割网络,对区域内的鱼类实现较高精度图像分割。实验结果表明,本文方法在以VGG-16、ResNet-50和ResNet-101作为主干网络的模型上的分割精度为81.75%、83.73%和85.66%。其中,以ResNet-101作为主干网络的模型与Mask R-CNN、U-Net、DeepLabv3相比,分割精度分别高14.24、11.36、9.45个百分点。本文方法可以为鱼类生物学信息的自动提取提供技术参考。

An edge-adaptive demosaicking method based on image correlation

To reduce the cost, size and complexity, a consumer digital camera usually uses a single sensor overlaid with a color filter array(CFA) to sample one of the red-green-blue primary color values, and uses demosaicking algorithm to estimate the missing color values at each pixel. A novel image correlation and support vector machine(SVM) based edge-adaptive algorithm was proposed, which can reduce edge artifacts and false color artifacts, effectively. Firstly, image pixels were separated into edge region and smooth region with an edge detection algorithm. Then, a hybrid approach switching between a simple demosaicking algorithm on the smooth region and SVM based demosaicking algorithm on the edge region was performed. Image spatial and spectral correlations were employed to create middle planes for the interpolation. Experimental result shows that the proposed approach produced visually pleasing full-color result images and obtained higher CPSNR and smaller S-CIELAB*ab?E than other conventional demosaicking algorithms.

5G核心网网络架构及其关键技术

随着5G技术在商业领域中应用的不断推进,行业中关于5G核心网的研究也越来越广泛,成果越来越显著。文章对5G核心网的网络架构展开分析,并重点围绕服务化架构、支持边缘计算、容器技术、网络切片等方向探讨5G核心网关键技术,旨在推动5G核心网的进一步发展和广泛运用。

5G核心网关键技术分析

随着5G商用的不断推进,关于5G核心网的相关研究日渐引起行业重视。基于此,本文将简单介绍5G核心网的整体架构,深入探讨5G核心网的关键技术,并以此提出相关建议。