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Binary Image Based Fast DoG Filter Using Zero-Dimensional Convolution and State Machine LUTs

Authors
SEUNG-JUN LEEKYE-SHIN LEE김병규
Issue Date
Jun-2018
Publisher
한국멀티미디어학회
Keywords
Binary Image; DoG Filter; Look-up Tables(LUTs); Zero Dimensional Convolution
Citation
The Journal of Multimedia Information System, v.5, no.2, pp 131 - 138
Pages
8
Journal Title
The Journal of Multimedia Information System
Volume
5
Number
2
Start Page
131
End Page
138
URI
https://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/4437
DOI
10.9717/JMIS.2018.5.2.131
ISSN
2383-7632
Abstract
This work describes a binary image based fast Difference of Gaussian (DoG) filter using zero-dimensional (0-d) convolution and state machine look up tables (LUTs) for image and video stitching hardware platforms. The proposed approach for using binary images to obtain DoG filtering can significantly reduce the data size compared to conventional gray scale based DoG filters, yet binary images still preserve the key features of the image such as contours, edges, and corners. Furthermore, the binary image based DoG filtering can be realized with zero-dimensional convolution and state machine LUTs which eliminates the major portion of the adder and multiplier blocks that are generally used in conventional DoG filter hardware engines. This enables fast computation time along with the data size reduction which can lead to compact and low power image and video stitching hardware blocks. The proposed DoG filter using binary images has been implemented with a FPGA (Altera DE2-115), and the results have been verified.
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