基于连续尺度空间和多特征提取的煤矿监控视频注意计算模型被引量：2

Computation model of visual attention for coal-mine surveillance video based on sequential scale space and multi-features

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摘要在分析当前自底向上注意计算模型在煤矿图像中应用缺陷的基础上,受Itti模型的启发,提出了一种新的基于多尺度和多特征的煤矿监控视频注意计算模型。在多尺度分析上,以连续尺度的离散化形式为非均匀采样的表达,以Bessel修正函数为滤波核;在特征提取中,采用运动显著性,小波包分解和亮度为待注意目标的显著性度量,采用DOG算子对各种特征进行归一化,最终形成统一的待注意目标显著图。实验结果证明了算法的适应性和高效性。 Analyzed the limitations of existing down-top attention model in the application of coal mine surveillance video,then proposed a new computation model of visual attention aiming at coal-mine surveillance video based on sequential scale space and multi-features.Different from the existing model,the expression of non-uniform sampling was based on the discrete structure of sequential scale space,and chose the modified Bessel function as the smooth kernel.About feature extraction,chose the motion conspicuity,wavelet package decomposition and gray intensity as measures of saliency,and adopted DOG（Difference of Gaussian） operator as the generalized method.Finally,a global saliency map for the interesting objects was formed.The experiment results show the flexibility and effectiveness of this model.

作者周磊华钢徐钊徐冬梅李登旺

机构地区中国矿业大学信息与电气工程学院悉尼大学信息技术学院山东大学信息科学与工程学院

出处《煤炭学报》 EI CAS CSCD 北大核心 2010年第8期1406-1411,共6页 Journal of China Coal Society

基金国家自然科学基金重点资助项目(50534050)

关键词连续尺度多特征煤矿监控视频注意机制计算模型 sequential scale multi-features coal-mine surveillance video attention computation model

分类号 TD65 [矿业工程—矿山机电]

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