您正在使用 kernel_size = 3,而它对上述场景来说太小了。在上面的图像中,kernel_size = 3 主要受到噪声的影响,因为边缘(在显示更多细节的图像中)比 3x3 像素大得多。换句话说,细节的“特殊频率”是低频的,而 3x3 核强调了更高的特殊频率。 可能的解决方案: 1、您可以增加核大小-例如设置 kernel_size = ...
先创建出上面那个downsampling用的kernel: defcall2dtensorgaussfilter():return tf.constant([[1./256.,4./256.,6./256.,4./256.,1./256.],[4./256.,16./256.,24./256.,16./256.,4./256.],[6./256.,24./256.,36./256.,24./256.,6./256.],[4./256.,16./256.,24./256.,16....
论文:Gaussian YOLOv3: An Accurate and Fast Object Detector Using Localization Uncertainty for Autonomo 最近发现了好多基于YOLO修改的网络,一般都是‘剪枝’,已达到轻量级的目的 在目标检测的落地项目中,实时性和精确性的trade-off至关重要,而YOLOv3是目前为止在这方面做得最好的算法。本文通过高斯分布的特性,改...
最后Laplacian算子不能检测边缘的方向;所以Laplacian在分割中所起的作用包括:(1)利用它的零交叉性质进行边缘定位;(2)确定一个像素是在一条边缘暗的一面还是亮的一面;一般使用的是高斯型拉普拉斯算子(Laplacian of a Gaussian,LoG),由于二阶导数是线性运算,利用LoG卷积一幅图像与首先使用高斯型平滑函数卷积改图像,...
In the above images,kernel_size = 3is affected mostly bynoise, because the edges (in the image that shows more details) are much larger than 3x3 pixels. In other words, the "special frequency" of the details is low frequency, and the 3x3 kernel emphasizes much higher special freq...