通过图像灰度与温度的相关性,可以精确地得到一些领域上难以被测量到的温度。提出了基于红外靶标的图像灰度与温度的相关性分析。以自行研制的红外靶标为研究对象,采集带有圆形目标孔的红外靶标在不同温度（60-110℃）下的红外图像,再利...通过图像灰度与温度的相关性,可以精确地得到一些领域上难以被测量到的温度。提出了基于红外靶标的图像灰度与温度的相关性分析。以自行研制的红外靶标为研究对象,采集带有圆形目标孔的红外靶标在不同温度（60-110℃）下的红外图像,再利用Matlab提取不同温度下特定区域的红外图像灰度值,从而确定红外靶标图像灰度与其温度的相关性,得其相关系数为0.962。实验结果表明：红外靶标图像中AOI（area of interest）的平均灰度与温度存在明显的相关性,当红外靶标的温度发生变化时,其红外图像灰度也随之发生变化,且两者呈良好的线性关系。基于红外靶标的图像灰度与温度的相关性在造纸厂草料自燃研究、医疗安全、道路施工等方面都有着较好的应用。展开更多
A fast algorithm based on the grayscale distribution of infrared target and the weighted kernel function was proposed for the moving target detection(MTD) in dynamic scene of image series. This algorithm is used to de...A fast algorithm based on the grayscale distribution of infrared target and the weighted kernel function was proposed for the moving target detection(MTD) in dynamic scene of image series. This algorithm is used to deal with issues like the large computational complexity, the fluctuation of grayscale, and the noise in infrared images. Four characteristic points were selected by analyzing the grayscale distribution in infrared image, of which the series was quickly matched with an affine transformation model. The image was then divided into 32×32 squares and the gray-weighted kernel(GWK) for each square was calculated. At last, the MTD was carried out according to the variation of the four GWKs. The results indicate that the MTD can be achieved in real time using the algorithm with the fluctuations of grayscale and noise can be effectively suppressed. The detection probability is greater than 90% with the false alarm rate lower than 5% when the calculation time is less than 40 ms.展开更多
文摘通过图像灰度与温度的相关性,可以精确地得到一些领域上难以被测量到的温度。提出了基于红外靶标的图像灰度与温度的相关性分析。以自行研制的红外靶标为研究对象,采集带有圆形目标孔的红外靶标在不同温度（60-110℃）下的红外图像,再利用Matlab提取不同温度下特定区域的红外图像灰度值,从而确定红外靶标图像灰度与其温度的相关性,得其相关系数为0.962。实验结果表明：红外靶标图像中AOI（area of interest）的平均灰度与温度存在明显的相关性,当红外靶标的温度发生变化时,其红外图像灰度也随之发生变化,且两者呈良好的线性关系。基于红外靶标的图像灰度与温度的相关性在造纸厂草料自燃研究、医疗安全、道路施工等方面都有着较好的应用。
基金Project(61101185)supported by the National Natural Science Foundation of China
文摘A fast algorithm based on the grayscale distribution of infrared target and the weighted kernel function was proposed for the moving target detection(MTD) in dynamic scene of image series. This algorithm is used to deal with issues like the large computational complexity, the fluctuation of grayscale, and the noise in infrared images. Four characteristic points were selected by analyzing the grayscale distribution in infrared image, of which the series was quickly matched with an affine transformation model. The image was then divided into 32×32 squares and the gray-weighted kernel(GWK) for each square was calculated. At last, the MTD was carried out according to the variation of the four GWKs. The results indicate that the MTD can be achieved in real time using the algorithm with the fluctuations of grayscale and noise can be effectively suppressed. The detection probability is greater than 90% with the false alarm rate lower than 5% when the calculation time is less than 40 ms.
