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Received:August 15, 2012 Revised:September 23, 2012
Received:August 15, 2012 Revised:September 23, 2012
中文摘要: 针对带式输送机在运行过程中输送带易撕裂的背景, 根据人眼视觉系统特点和信息论相关理论, 提出了一种基于视觉显著性和一维最大熵的输送带撕裂故障检测算法. 首先在频域提取图像的显著性区域, 然后计算显著图的最大信息熵, 进而分割图像. 实验结果表明, 该方法具有较强的稳定性和适应性.
Abstract:Under the background that conveyor belts are prone to rips in operation, According to the characteristics of human visual system and information theory, an algorithm for detecting rips in conveyor belts based on visual saliency and maximal entropy is proposed. Regions of interest are firstly extracted in frequency domain, and then a threshold is obtained through calculating the maximal entropy of saliency map to segment the grayscale image. Experiments show that the algorithm is stable and adaptable.
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基金项目:国家自然科学基金(51274150);天津市自然科学基金(12JCZDJC27800)
引用文本：
亢伉,苗长云,杨彦利.基于显著性和最大熵的输送带撕裂检测.计算机系统应用,2013,22(3):117-120
KANG Kang,MIAO Chang-Yun,YANG Yan-Li.Detection Rips in Conveyor Belts Based on Saliency and Maximal Entropy.COMPUTER SYSTEMS APPLICATIONS,2013,22(3):117-120
亢伉,苗长云,杨彦利.基于显著性和最大熵的输送带撕裂检测.计算机系统应用,2013,22(3):117-120
KANG Kang,MIAO Chang-Yun,YANG Yan-Li.Detection Rips in Conveyor Belts Based on Saliency and Maximal Entropy.COMPUTER SYSTEMS APPLICATIONS,2013,22(3):117-120
