With the development of the economy and the surge in car ownership, the sale of used cars has been welcomed by more and more people, and the information of the vehicle condition is the focus information of them. The f...With the development of the economy and the surge in car ownership, the sale of used cars has been welcomed by more and more people, and the information of the vehicle condition is the focus information of them. The frame number is a unique number used in the vehicle, and by identifying it can quickly find out the vehicle models and manufacturers. The traditional character recognition method has the problem of complex feature extraction, and the convolutional neural network has unique advantages in processing two-dimensional images. This paper analyzed the key techniques of convolutional neural networks compared with traditional neural networks, and proposed improved methods for key technologies, thus increasing the recognition of characters and applying them to the recognition of frame number characters.展开更多
目的实时渲染图形程序(如游戏、虚拟现实等)对高分辨率和高刷新率的要求越来越高,因此,针对渲染图像的实时超分辨率技术在实时渲染中非常必要。然而,现有的视频超分算法和实时渲染处于不同的数据处理管线之中,这导致其难以直接应用到实...目的实时渲染图形程序(如游戏、虚拟现实等)对高分辨率和高刷新率的要求越来越高,因此,针对渲染图像的实时超分辨率技术在实时渲染中非常必要。然而,现有的视频超分算法和实时渲染处于不同的数据处理管线之中,这导致其难以直接应用到实时渲染管线里。方法对此,提出了一个基于帧循环结构的实时神经超采样方法。充分利用实时渲染管线中生成的低分辨场景几何数据,以提升超采样网络对于三维空间信息的感知力;将帧循环框架结合到超采样方法中,通过引入先前帧重建结果的特征来改善当前帧的重建结果,从而实现时间尺度上的稳定性;将重加权网络和注意力网络置于特征提取模块中,以提升提取到的特征的有效性。此外,本文还提出了一个面向神经超采样的实时渲染流程,该流程能够将超采样网络部署至图形计算管线之上,并与实时渲染管线相结合。结果与同样能够实时且效果较好的基准方法面向实时渲染的神经超采样(neural super-sampling for real-time rendering,NSRR)比较,本文方法在速度少许提升的前提下,图像质量指标峰值信噪比(peak signal to noise ratio,PSNR)平均提升了0.4 dB,并在部署到实时渲染管线后,通过轻量化裁剪继续保持实时性且部分场景效果仍然优于非实时的部署后NSRR;在网络模块的消融实验中也证明了各个子模块对于神经超采样任务的有效性。结论本文提出的神经超采样网络模型与搭建的神经超采样渲染流程,在取得更好效果的同时具有一定的实用价值。展开更多
文摘With the development of the economy and the surge in car ownership, the sale of used cars has been welcomed by more and more people, and the information of the vehicle condition is the focus information of them. The frame number is a unique number used in the vehicle, and by identifying it can quickly find out the vehicle models and manufacturers. The traditional character recognition method has the problem of complex feature extraction, and the convolutional neural network has unique advantages in processing two-dimensional images. This paper analyzed the key techniques of convolutional neural networks compared with traditional neural networks, and proposed improved methods for key technologies, thus increasing the recognition of characters and applying them to the recognition of frame number characters.
文摘目的实时渲染图形程序(如游戏、虚拟现实等)对高分辨率和高刷新率的要求越来越高,因此,针对渲染图像的实时超分辨率技术在实时渲染中非常必要。然而,现有的视频超分算法和实时渲染处于不同的数据处理管线之中,这导致其难以直接应用到实时渲染管线里。方法对此,提出了一个基于帧循环结构的实时神经超采样方法。充分利用实时渲染管线中生成的低分辨场景几何数据,以提升超采样网络对于三维空间信息的感知力;将帧循环框架结合到超采样方法中,通过引入先前帧重建结果的特征来改善当前帧的重建结果,从而实现时间尺度上的稳定性;将重加权网络和注意力网络置于特征提取模块中,以提升提取到的特征的有效性。此外,本文还提出了一个面向神经超采样的实时渲染流程,该流程能够将超采样网络部署至图形计算管线之上,并与实时渲染管线相结合。结果与同样能够实时且效果较好的基准方法面向实时渲染的神经超采样(neural super-sampling for real-time rendering,NSRR)比较,本文方法在速度少许提升的前提下,图像质量指标峰值信噪比(peak signal to noise ratio,PSNR)平均提升了0.4 dB,并在部署到实时渲染管线后,通过轻量化裁剪继续保持实时性且部分场景效果仍然优于非实时的部署后NSRR;在网络模块的消融实验中也证明了各个子模块对于神经超采样任务的有效性。结论本文提出的神经超采样网络模型与搭建的神经超采样渲染流程,在取得更好效果的同时具有一定的实用价值。
