A Linked List-Based Algorithm for Blob Detection on Embedded Vision-Based Sensors
AbstractBlob 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
Share & Cite This Article
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.
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(6):782.Chicago/Turabian Style
Acevedo-Avila, Ricardo; Gonzalez-Mendoza, Miguel; Garcia-Garcia, Andres. 2016. "A Linked List-Based Algorithm for Blob Detection on Embedded Vision-Based Sensors." Sensors 16, no. 6: 782.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.