Measurement of the high energyγ-rays from heavy ion reactions usingČerenkov detector

The energetic bremsstrahlung photons up to 100 MeV produced in heavy ion collisions can be used as a sensitive probe for short-range correlation in atomic nuclei. The energy of the γ-rays can be measured by collecting the Čerenkov light in the medium induced by the fast electrons generated in the Compton scattering or electromagnetic shower of the incident γray. Two types of detectors based on pure water and lead glass as sensitive materials were designed for this purpose. The γresponse and optical photon propagation in the detectors were simulated based on electromagnetic and optical processes in Geant4. The inherent energy resolutions of 0.022(4) + 0.51(2)∕E^(1/2)_(γ) for water and 0.0026(3) + 0.446(3)∕E^(1/2)_(γ) for lead glass were obtained. The geometry sizes of the lead glass and water were optimized to 30 cm × 30 cm × 30 cm and 60 cm × 60 cm ×120 cm, respectively, to detect high-energy γ-rays at 160 MeV. The Hough transform method was applied to reconstruct the direction of the incident γ-rays, providing the ability to experimentally distinguish the high-energy γ-rays produced in the reactions on the target from random background cosmic-ray muons.

一种基于新三维随机Hough变换的航迹起始算法

针对雷达跟踪多目标时,目标点迹受杂波、噪声等因素影响,航迹起始难度大的问题,利用三维空间直线表示方法提出了一种4参数三维Hough变换算法。该算法是对传统二维Hough变换的拓展,它结合了传统的二维随机Hough变换理论,是一种新的三维随机Hough变换算法。通过该算法对理论数据和实测数据进行验证,结果表明,对于航迹区别较大的目标,该算法的航迹起始成功概率为98.5%;对于空间航迹相近的目标,该算法可成功将目标航迹从杂波中提取出来,虽然可能会出现航迹混淆,但利用目标先验信息可解决该问题,实现航迹起始。

复杂背景下电力线自动提取算法

为从具有复杂背景的无人机航拍图像中准确完整地提取电力线,提出了一种结合改进Ratio算子和改进Hough变换的电力线提取新方法,命名为基于比率算子聚类和霍夫变换编组的方法(ratio-basedclusteringand hough-basedgrouping,RCHG)。首先采用Ratio算子进行电力线边缘检测,在综合考虑电力线连续性和噪声抑制能力的理念下,给出了线特征检测阈值的参考范围。然后,对边缘图像进行四连通聚类分析,消除大部分背景噪声。最后,采用Hough变换提取电力线,进一步设计直线段聚类算法,对提取结果进行直线编组并进行最小二乘拟合处理,以解决电力线断裂和重叠的问题。实验结果表明,相比传统Ratio算子结合Hough变换的方法及Line Segment Detector(LSD)算法,所提方法具有更好的抗噪性能和更高的电力线提取精度,能从具有复杂背景的无人机航拍图像中准确完整地提取出电力线,有较高的工程应用价值。

基于边缘分布函数的车道标识线识别方法

为了得到较理想的车道标识线的边缘,考虑车道标识线的方向特性,提出一种基于边缘分布函数(EDF)的图像预处理方法。将图像分区处理,在对图像中的噪声特性进行EDF分析的基础上,对处理区域作如下处理:首先将梯度角量化为4-方向,去除与车道标识线方向不一致的噪声,得到边缘图像;然后利用EDF对边缘图像滤波,确定车道标识线角度初值;最后应用Hough变换定位出车道标识线。实验结果表明,该方法能够更加有效地强化车道标识线信息,去除噪声,具有较好的鲁棒性和实时性。

一种基于改进ZS细化算法的指针仪表检测

针对ZS细化算法存在二像素畸变、细化不彻底等问题,提出一种改进的细化算法。对二值图像采用ZS细化算法进行初步细化,将初步细化后的结果进行全细化识别,依据识别结果对未完全细化的图像采用4连通细化模板实现彻底细化。同时对细化过程可能出现的断点问题,采用构造保留模版的方法加以解决,进而实现目标图像的有效细化。以油量表为例,结合改进细化算法实现仪表读数识别。仿真结果表明,改进细化算法在保持原有ZS细化算法快速性的基础上可有效地解决二像素畸变、像素冗余的问题,并实现准确的指针读数识别。

航迹起始算法及其性能仿真

针对密集杂波环境中目标航迹的快速起始问题,提出一种新的航迹起始算法,该算法结合Hough变换和逻辑法两种算法的优点,仿真试验结果表明,同逻辑法相比该算法虚假航迹数量明显下降,运算量没有显著增加,有较高的工程应用价值。