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Sensors 2016, 16(6), 782;

A Linked List-Based Algorithm for Blob Detection on Embedded Vision-Based Sensors

Department of Postgraduate Studies, Tecnológico de Monterrey, Campus Estado de México, Atizapán de Zaragoza, Estado de México 52926, Mexico
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editors: Lourdes Martínez-Villaseñor and Hiram Ponce
Received: 5 February 2016 / Revised: 17 May 2016 / Accepted: 25 May 2016 / Published: 28 May 2016
PDF [8096 KB, uploaded 28 May 2016]


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. View Full-Text
Keywords: embedded computer vision; field programmable gate array (FPGA); object detection embedded computer vision; field programmable gate array (FPGA); object detection

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Acevedo-Avila, R.; Gonzalez-Mendoza, M.; Garcia-Garcia, A. A Linked List-Based Algorithm for Blob Detection on Embedded Vision-Based Sensors. Sensors 2016, 16, 782.

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