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  • Open Access


    An Efficient Bar Code Image Recognition Algorithm for Sorting System

    Desheng Zheng1, *, Ziyong Ran1, Zhifeng Liu1, Liang Li2, Lulu Tian3

    CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1885-1895, 2020, DOI:10.32604/cmc.2020.010070

    Abstract In the sorting system of the production line, the object movement, fixed angle of view, light intensity and other reasons lead to obscure blurred images. It results in bar code recognition rate being low and real time being poor. Aiming at the above problems, a progressive bar code compressed recognition algorithm is proposed. First, assuming that the source image is not tilted, use the direct recognition method to quickly identify the compressed source image. Failure indicates that the compression ratio is improper or the image is skewed. Then, the source image is enhanced to identify More >

  • Open Access


    Image Recognition of Breast Tumor Proliferation Level Based on Convolution Neural Network

    Junhao Yang1, Chunxiao Chen1,*, Qingyang Zang1, Jianfei Li1

    Molecular & Cellular Biomechanics, Vol.15, No.4, pp. 203-214, 2018, DOI:10.32604/mcb.2018.03824

    Abstract Pathological slide is increasingly applied in the diagnosis of breast tumors despite the issues of large amount of data, slow viewing and high subjectivity. To overcome these problems, a micrograph recognition method based on convolutional neural network is proposed for pathological slide of breast tumor. Combined with multi-channel threshold and watershed segmentation, a sample database including single cell, adhesive cell and invalid cell was established. Then, the convolution neural network with six layers is constructed, which has ability to classify the stained breast tumor cells with accuracy of more than 90%, and evaluate the proliferation More >

  • Open Access


    A Method for Improving CNN-Based Image Recognition Using DCGAN

    Wei Fang1,2, Feihong Zhang1,*, Victor S. Sheng3, Yewen Ding1

    CMC-Computers, Materials & Continua, Vol.57, No.1, pp. 167-178, 2018, DOI:10.32604/cmc.2018.02356

    Abstract Image recognition has always been a hot research topic in the scientific community and industry. The emergence of convolutional neural networks(CNN) has made this technology turned into research focus on the field of computer vision, especially in image recognition. But it makes the recognition result largely dependent on the number and quality of training samples. Recently, DCGAN has become a frontier method for generating images, sounds, and videos. In this paper, DCGAN is used to generate sample that is difficult to collect and proposed an efficient design method of generating model. We combine DCGAN with More >

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