2016 International Conference on Frontiers of Information Technology (FIT) 2016
DOI: 10.1109/fit.2016.7857510
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FPGA implementation of template matching using binary sum of absolute difference

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Cited by 2 publications
(3 citation statements)
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“…(Zrilic, 2006). Therein, the feedback control combined with the incremental encoder and the absolute magnetic encoder can effectively eliminate the real-time positioning error from the change in the pulse width (Banerjee et al , 2016; Putra et al , 2016). Furthermore, the high-speed and high-precision servo control system contributes to not only guaranteeing the accuracy of servo motor but also optimizing the whole transmission system (Cetinkunt, 1991).…”
Section: Comparative-analysis Of Various Indoor-orbital Electrical In...mentioning
confidence: 99%
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“…(Zrilic, 2006). Therein, the feedback control combined with the incremental encoder and the absolute magnetic encoder can effectively eliminate the real-time positioning error from the change in the pulse width (Banerjee et al , 2016; Putra et al , 2016). Furthermore, the high-speed and high-precision servo control system contributes to not only guaranteeing the accuracy of servo motor but also optimizing the whole transmission system (Cetinkunt, 1991).…”
Section: Comparative-analysis Of Various Indoor-orbital Electrical In...mentioning
confidence: 99%
“…The morphological gradient can obtain the edge detection of the source image and template image by comparing between the dilated image and the eroded image by which the strengthen intensity of pixels can highlight the periphery of the edge area (Cheng and Xiang, 2019; Shi et al , 2021; Jadin and Taib, 2012). Thereafter, the grayscale-based template matching algorithm (Putra et al , 2016) can accomplish the high-precision matching between the source image and the template image (Cheng and Xiang, 2019; Brunelli, 2009). The corresponding calculations of the template matching algorithm are listed in Table 2.…”
Section: Comparative-analysis Of Various Indoor-orbital Electrical In...mentioning
confidence: 99%
“…It is a method that sums the total differences of the gray level of each pixel between the template and the examined image. Suppose T is the template of size m × n and E is the examined image of size u × v, where u > m and v > n. SAD at point (x, y) in the examined image is defined as follows [33,34]:…”
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confidence: 99%