Acevedo-Avila Ricardo, Gonzalez-Mendoza Miguel, Garcia-Garcia Andres
Department of Postgraduate Studies, Tecnológico de Monterrey, Campus Estado de México, Atizapán de Zaragoza, Estado de México 52926, Mexico.
Sensors (Basel). 2016 May 28;16(6):782. doi: 10.3390/s16060782.
Blob detection is a common task in vision-based applications. Most existing algorithms are aimed at execution on general purpose computers; while very few can be adapted to the computing restrictions present in embedded platforms. This paper focuses on the design of an algorithm capable of real-time blob detection that minimizes system memory consumption. The proposed algorithm detects objects in one image scan; it is based on a linked-list data structure tree used to label blobs depending on their shape and node information. An example application showing the results of a blob detection co-processor has been built on a low-powered field programmable gate array hardware as a step towards developing a smart video surveillance system. The detection method is intended for general purpose application. As such, several test cases focused on character recognition are also examined. The results obtained present a fair trade-off between accuracy and memory requirements; and prove the validity of the proposed approach for real-time implementation on resource-constrained computing platforms.
斑点检测是基于视觉的应用中的一项常见任务。大多数现有算法旨在在通用计算机上执行；而很少有算法能适应嵌入式平台存在的计算限制。本文重点设计一种能够进行实时斑点检测的算法，该算法可将系统内存消耗降至最低。所提出的算法在一次图像扫描中检测物体；它基于一种链表数据结构树，该树用于根据斑点的形状和节点信息对其进行标记。作为开发智能视频监控系统的一步，已在低功耗现场可编程门阵列硬件上构建了一个展示斑点检测协处理器结果的示例应用。该检测方法旨在用于通用应用。因此，还研究了几个专注于字符识别的测试用例。所获得的结果在准确性和内存需求之间实现了合理的权衡；并证明了所提出的方法在资源受限的计算平台上进行实时实现的有效性。
