In this paper, we conduct research on the natural image classification and segmentation algorithm based on GPU and neural network. The application of image segmentation is very broad, almost appeared in all areas rela...In this paper, we conduct research on the natural image classification and segmentation algorithm based on GPU and neural network. The application of image segmentation is very broad, almost appeared in all areas related to image processing, and involved in various types. With the fast development of computing technology and integrated circuit technology, the renewal speed of graphics hardware. Our method combines the GPU with network to optimize the traditional image segmentation and classification methods which will be meaningful. In the future, we will focus our attention on the hardware deployment of the GPU to modify the current approach.展开更多
为解决目前已有的图像匹配算法不适用于对实时性要求很强的应用,提出了PLS(Partial Least Squares)与余弦定理相结合的并行化图像匹配算法。该算法在CUDA架构下,对图像矩阵分块,分块后每个小块图像存入共享存储器处理并提取每个小块图...为解决目前已有的图像匹配算法不适用于对实时性要求很强的应用,提出了PLS(Partial Least Squares)与余弦定理相结合的并行化图像匹配算法。该算法在CUDA架构下,对图像矩阵分块,分块后每个小块图像存入共享存储器处理并提取每个小块图像特征,通过合并后图像特征采用余弦定理计算图像的相似度,从而找出匹配图像。实验表明,CUDA架构下可以实现图像的并行匹配,与CPU上串行匹配相比,时效性提高了百倍以上。展开更多
文摘In this paper, we conduct research on the natural image classification and segmentation algorithm based on GPU and neural network. The application of image segmentation is very broad, almost appeared in all areas related to image processing, and involved in various types. With the fast development of computing technology and integrated circuit technology, the renewal speed of graphics hardware. Our method combines the GPU with network to optimize the traditional image segmentation and classification methods which will be meaningful. In the future, we will focus our attention on the hardware deployment of the GPU to modify the current approach.
文摘为解决目前已有的图像匹配算法不适用于对实时性要求很强的应用,提出了PLS(Partial Least Squares)与余弦定理相结合的并行化图像匹配算法。该算法在CUDA架构下,对图像矩阵分块,分块后每个小块图像存入共享存储器处理并提取每个小块图像特征,通过合并后图像特征采用余弦定理计算图像的相似度,从而找出匹配图像。实验表明,CUDA架构下可以实现图像的并行匹配,与CPU上串行匹配相比,时效性提高了百倍以上。
