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计算机工程 ›› 2009, Vol. 35 ›› Issue (15): 173-175. doi: 10.3969/j.issn.1000-3428.2009.15.060

• 人工智能及识别技术 • 上一篇    下一篇

基于统计量的计算机图形检测模型

张 震1,2,边玉琨1,平西建2,康吉全1   

  1. (1. 郑州大学电气工程学院,郑州 450001;2. 解放军信息工程大学信息工程学院,郑州 450002)
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2009-08-05 发布日期:2009-08-05

Computer Graphics Detection Model Based on Statistics

ZHANG Zhen1,2, BIAN Yu-kun1, PING Xi-jian2, KANG Ji-quan1   

  1. (1. School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001; 2. Institute of Information Engineering, PLA Information Engineering University, Zhengzhou 450002)
  • Received:1900-01-01 Revised:1900-01-01 Online:2009-08-05 Published:2009-08-05

摘要: 自然图像和计算机图形的鉴别可采用模式识别的方法。采用统计矩特征量和基于颜色滤波阵列的统计量来建立模型,以捕获自然图像和计算机图形在图像内容上的不同相关性。选用哥伦比亚大学自然图像和计算机图形数据库来测试该模型,采用人工神经网络作为分类器进行训练和测试。实验结果表明,该模型的识别率高,稳定性好,具有较好的应用前景。

关键词: 自然图像, 计算机图形, 统计矩特征, 颜色滤波阵列, 人工神经网络

Abstract: Pattern recognition method can be used to discriminate photo images from computer graphics. In this paper, the model is established based on statistical moment features and statistics extracted from Color Filter Array(CFA). This model can capture different correlation of image content between photo images and Computer Graphics(CG). To evaluate the performance of the scheme, the model was further tested applying to the Columbia photographic images and photorealistic computer graphics dataset. Artificial neural network is chosen as a classifier to train and test the given images. Experimental results demonstrate that the model performs higher degree of accuracy, indicating that the proposed approach has better stability and possesses promising capability in discrimination of computer graphics from photo images.

Key words: photo image, Computer Graphics(CG), statistical moment feature, Color Filter Array(CFA), artificial neural network

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