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Threshold optimization selection of fast multimedia image segmentation processing based on Labview

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Abstract

With the continuous improvement of computer technology information level, multimedia image processing technology is constantly updating and progressing, and it is more and more urgent to quickly perform multimedia image recognition processing. Multimedia image recognition is an important issue in image processing. Image segmentation is the basic premise for visual analysis and pattern recognition of multimedia images. The multimedia image recognition segmentation algorithm based on threshold selection is simple in calculation and has high computational efficiency, which makes it widely used in multimedia real-time image processing systems. However, due to the variety of threshold selection, it directly affects multimedia image segmentation effectiveness. In this paper, the research and discussion on some features of the multimedia image segmentation recognition algorithm based on threshold selection and its application are carried out. The segmentation effect of the maximum entropy method and the operation time of logarithmic entropy are studied. Then, the exponential entropy is used instead of the pair. The numerical entropy is improved, and the two-dimensional maximum entropy method is improved. Combined with the Otsu method, the information of the gray level of the 4 neighbourhood pixels is added. Experimental results show that the method used in this paper can effectively shorten the calculation time, highlight the edge features, and increase the threshold automatic selection accuracy and robustness.

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Authors and Affiliations

  1. Jiangsu Agri-animal Husbandry Vocational College, Taizhou, 225300, Jiangsu, China

    Rong Chen

  2. College of Information Engineering, Yangzhou University, Yangzhou, 225000, Jiangsu, China

    Yong-an Xu

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  1. Rong Chen
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  2. Yong-an Xu
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Correspondence to Rong Chen.

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Chen, R., Xu, Ya. Threshold optimization selection of fast multimedia image segmentation processing based on Labview. Multimed Tools Appl 79, 9451–9467 (2020). https://doi.org/10.1007/s11042-019-07775-y

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  • DOI: https://doi.org/10.1007/s11042-019-07775-y

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