This letter proposes a novel partial pattern classification system that uses local binary patterns as a classifier and a content-addressable memory, which has the parallel search capability, to perform classification at a higher speed. The proposed pattern classification system uses Manhattan distance for class assignment and further uses logical resources on the Xilinx Virtex-7 field-programmable gate array to perform classification. Our proposed system assigns a class to a pattern of any size and shape in as less as $1.12~\mu \text{s}$ , which is 33% faster than the state-of-the-art pattern classification system.

中文翻译：

---

在 FPGA 上使用基于 CAM 的 LBP 直方图的高速部分模式分类系统

这封信提出了一种新颖的部分模式分类系统，该系统使用局部二进制模式作为分类器和具有并行搜索能力的内容可寻址存储器，以更快地执行分类。提议的模式分类系统使用曼哈顿距离进行类别分配，并进一步使用赛灵思 Virtex-7 现场可编程门阵列上的逻辑资源来执行分类。我们提出的系统将一个类分配给任何大小和形状的图案 $1.12~\mu \text{s}$ ，比最先进的模式分类系统快 33%。