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      Influence of Embedded Microprocessor Wireless Communication and Computer Vision in Wushu Competition Referees’ Decision Support

      1 , 2 , 3
      Wireless Communications and Mobile Computing
      Hindawi Limited

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          Abstract

          With the development of computer technology and management science, decision support systems have emerged that can improve the quality and effects of decision-making. This study mainly examined the application of the wireless communication of an embedded microprocessor and computer vision in the decision support system of martial arts competition referees. Using the embedded microprocessor’s characteristics of a small size, high precision, high reliability, and high efficiency, a decision support system for martial arts competition referees was designed. In the experiment, the similarity between the target field and the source field could be controlled by adjusting the mean value. To better extract the target, this study used the time domain changes of the three adjacent frames, before, middle, and back, to detect the moving target to extract the change detection template. The Canny edge detection method was used to extract the edge information of the image and eliminate the nonmotion area; then, morphology was used to correct the image to complete the connection of the broken edge to obtain the final initial segmentation mask image. In the process of calculation, there were some noises and small fragments. In this study, morphology and background difference were used to optimize the segmented image. Experimental data show that the algorithm detection accuracy rate was high—between 70% and 100%—and the effect was relatively ideal. The results indicate that the proposed algorithm can effectively reduce matching noise, improve the matching accuracy of the edge area and the low-texture area, and achieve a fast matching speed.

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          Deep Convolutional Neural Networks with transfer learning for computer vision-based data-driven pavement distress detection

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            Plant Species Identification Using Computer Vision Techniques: A Systematic Literature Review

            Species knowledge is essential for protecting biodiversity. The identification of plants by conventional keys is complex, time consuming, and due to the use of specific botanical terms frustrating for non-experts. This creates a hard to overcome hurdle for novices interested in acquiring species knowledge. Today, there is an increasing interest in automating the process of species identification. The availability and ubiquity of relevant technologies, such as, digital cameras and mobile devices, the remote access to databases, new techniques in image processing and pattern recognition let the idea of automated species identification become reality. This paper is the first systematic literature review with the aim of a thorough analysis and comparison of primary studies on computer vision approaches for plant species identification. We identified 120 peer-reviewed studies, selected through a multi-stage process, published in the last 10 years (2005–2015). After a careful analysis of these studies, we describe the applied methods categorized according to the studied plant organ, and the studied features, i.e., shape, texture, color, margin, and vein structure. Furthermore, we compare methods based on classification accuracy achieved on publicly available datasets. Our results are relevant to researches in ecology as well as computer vision for their ongoing research. The systematic and concise overview will also be helpful for beginners in those research fields, as they can use the comparable analyses of applied methods as a guide in this complex activity.
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              Computer vision for SHM of civil infrastructure: From dynamic response measurement to damage detection – A review

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                Author and article information

                Contributors
                Journal
                Wireless Communications and Mobile Computing
                Wireless Communications and Mobile Computing
                Hindawi Limited
                1530-8677
                1530-8669
                January 17 2022
                January 17 2022
                : 2022
                : 1-13
                Affiliations
                [1 ]Department of Physical Education, Southeast University, Nanjing, 211189 Jiangsu, China
                [2 ]Guilin Tourism University, Guili, 541000 Guangxi, China
                [3 ]Institute of Sports Economic Theory, East China University of Science and Technology, Shanghai 200237, China
                Article
                10.1155/2022/2121573
                0178e18e-4c15-4ee3-a158-dc0eea7847b6
                © 2022

                https://creativecommons.org/licenses/by/4.0/

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