Stereo correspondence is a well-established research topic and has spawned categories of algorithms combining several processing steps and strategies. One core part to stereo correspondence is to determine matching cost between the two images, or patches from the two images. Over the years several different cost metrics have been proposed, one being the Census Transform (CT). The CT is well proven for its robust matching, especially along object boundaries, with respect to outliers and radiometric differences. The CT also comes at a low computational cost and is suitable for hardware implementation. Two key developments to the CT are non-centric and sparse comparison schemas, to increase matching performance and/or save computational resources. Recent CT algorithms share both traits but are handcrafted, bounded with respect to symmetry, edge lengths and defined for a specific window size. To overcome this, a Genetic Algorithm (GA) was applied to the CT, proposing the Genetic Algorithm Census Transform (GACT), to automatically derive comparison schemas from example data. In this paper, FPGA-based hardware acceleration of GACT, has enabled evaluation of census windows of different size and shape, by significantly reducing processing time associated with training. The experiments show that lateral GACT windows produce better matching accuracy and require less resources when compared to square windows.

立体对应是一个公认的研究主题，并产生了结合几种处理步骤和策略的算法类别。立体对应的一个核心部分是确定两个图像之间的匹配成本，或确定来自两个图像的色块。多年来，已经提出了几种不同的成本指标，一种是人口普查转换（CT）。CT因其在异常值和辐射度差异方面的鲁棒匹配（尤其是沿着对象边界）而得到充分证明。CT的计算成本也很低，适合硬件实施。CT的两个关键发展是非中心和稀疏的比较方案，以提高匹配性能和/或节省计算资源。最近的CT算法具有两个特征，但都是手工制作的，在对称性方面受到限制，边缘长度，并为特定的窗口大小定义。为了克服这个问题，将遗传算法（GA）应用于CT，提出了遗传算法普查变换（GACT），以自动从示例数据中得出比较方案。在本文中，基于FPGA的GACT硬件加速通过显着减少与训练相关的处理时间，可以评估不同大小和形状的普查窗口。实验表明，与方形窗口相比，横向GACT窗口可产生更好的匹配精度，并且所需资源更少。通过大大减少与培训相关的处理时间，可以评估不同大小和形状的普查窗口。实验表明，与方形窗口相比，横向GACT窗口可产生更好的匹配精度，并且所需资源更少。通过显着减少与培训相关的处理时间，可以评估不同大小和形状的普查窗口。实验表明，与方形窗口相比，横向GACT窗口可产生更好的匹配精度，并且所需资源更少。