论文标题
使用加速框架在立体声图像中基于深度的选择性模糊
Depth-Based Selective Blurring in Stereo Images Using Accelerated Framework
论文作者
论文摘要
我们提出了一种混合方法来通过结合块和基于区域的立体声匹配方法来进行立体声差异估计。它从仅18％图像像素（左或右）的差异测量中生成密集的深度图。该方法涉及使用快速K-均值实施的像素光值分割，使用形态滤波和连接的组件分析来精炼段边界；然后使用绝对差异（SAD）成本函数的总和来确定边界的差异。从边界的差异重建了完整的差异图。我们考虑使用高斯模糊来脱离对焦用户的非利息区域的方法，用于基于深度的基于深度的选择性模糊。 Middlebury数据集的实验表明，我们的方法使用SAD和归一化的跨相关性的传统差异估计方法高达33.6％，而最近的一些方法则高达6.1％。此外，使用基于Java线程池和Aparapi的CPU和GPU框架，我们的方法高度可行，250立体声视频帧的加速度为5.8（4,096 x 2,304）。
We propose a hybrid method for stereo disparity estimation by combining block and region-based stereo matching approaches. It generates dense depth maps from disparity measurements of only 18 % image pixels (left or right). The methodology involves segmenting pixel lightness values using fast K-Means implementation, refining segment boundaries using morphological filtering and connected components analysis; then determining boundaries' disparities using sum of absolute differences (SAD) cost function. Complete disparity maps are reconstructed from boundaries' disparities. We consider an application of our method for depth-based selective blurring of non-interest regions of stereo images, using Gaussian blur to de-focus users' non-interest regions. Experiments on Middlebury dataset demonstrate that our method outperforms traditional disparity estimation approaches using SAD and normalized cross correlation by up to 33.6 % and some recent methods by up to 6.1 %. Further, our method is highly parallelizable using CPU and GPU framework based on Java Thread Pool and APARAPI with speed-up of 5.8 for 250 stereo video frames (4,096 x 2,304).
