因素空间理论下基点分类算法研究

目前，基于因素空间理论的背景基提取算法计算过程复杂，初始化必须依赖各因素极值，基点数量提取冗余等原因，未能在应用中取得很好效果。为此，结合内点判别法和知识可继承、可扩展的思想，提出一种计算简单、初始化独立、基点数量小的改进的背景基提取算法。然后，利用改进的背景基提取算法构造出一种全新的数据分类算法-基点分类算法，基点分类算法以提取每一类样本的背景基为预测模型，再通过新定义的λ-背景基，优化预测模型。数值实验表明:基点分类算法原理简单、构造难度小、分类模型泛化能力强，预测能力准确率高，同时严格的模型限定区域又能为识别新类别提供新方法。     

从事放射性个人剂量监测分析与防护调查

为了能及时了解放射性工作人员的受照剂量，以便能够更好地保障放射工作人员的健康与安全，采用热释光测量法对包头地区从事放射性工作的工作人员进行了个人剂量监测，并对他们的剂量当量频数分布进行了统计与分析，对工作环境的防护进行了调查对比，就今后的工作提出建议。     

红外背景干扰研究

根据从许多地区收集来的录象磁带上的图象数据和大量红外图象数据，对红外背景图象内的干扰进行了研究。图象内的噪声是以象元电平为单位的均方根形成计算。图象经过空间滤波处理，对其内的干扰再进行计算。为获得关于红外背景图象的空间频率构成的信息，在滤波和非滤波两种状态下计算了图象的光谱密度，把计算出的干扰值输入数据库取出多组数据并绘图，确定参数量（如天气、背景类型和测量条件等）对干扰电平的影响。     

Effects of surrounding brightness on visual search for safety colors

The present article reports the investigation of the effects of surrounding brightness on a visual search for three safety colors: red, orange, and yellow. Images of visual stimuli consisting of an array of colored circles placed on a large visual field (a visual angle of 40°) were displayed on an 80‐in. screen with a DLP projector. Experiment I examined the search efficiency under three levels of background luminance that were equivalent to the three target luminances. The results showed that the search efficiency for the orange target decreased as the number of distractors increased, under each of the background luminance levels, whereas the efficiency scarcely decreased for the red and yellow targets. Although a reduction in background luminance increased the search efficiency for the orange target, it is suggested that the effect of background luminance is smaller than the effect of the target color in search efficiency. Experiment II examined the search efficiency under three conditions of low levels of incident illuminance, which were matched with a linear regression to the luminance of color chips of safety colors measured twilight conditions. The results showed that the search efficiency for the orange target decreased as the number of distractors increased under each of the different illuminance conditions, whereas the efficiency scarcely decreased for red and yellow targets. Furthermore, as illuminance decreased, the search time for the orange target was more greatly impacted than for red or yellow. These results imply that the recognition of orange tends to be influenced by the surrounding brightness. © 2005 Wiley Periodicals, Inc. Col Res Appl, 30, 400–409, 2005; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.20152     

Complexity of the color appearance of objects with nonuniform illumination

An inversion relationship is clarified between lightness and brightness, and also between chroma and colorfulness under nonuniform illumination with different illuminance levels (high and low) within the same visual field. Brightness (or colorfulness) of object color with low lightness (or chroma) under high illuminance level is perceived higher than that of another object color with high lightness (or chroma) under low illuminance level. Two color images are given for showing the inversion phenomena on brightness–lightness and colorfulness–chroma between object colors under different illuminance levels. These color images are useful for making researchers on color understand the differences in concept between brightness and lightness, and also between colorfulness and chroma. In particular, the concept of colorfulness is important, but difficult to understand. © 2007 Wiley Periodicals, Inc. Col Res Appl, 32, 372–377, 2007     

变温长波碲镉汞光电导现象研究

报道了ｎ型Ｈｇ０．８Ｃｌ０．２Ｔｅ光电导体的变温材料参数与性能参数的对照关系，并讨论了材料的锭条参数与小芯片霍耳参数的差异，得到一组光电导的实验优化数据，实测小芯片载流子浓度ｎ≈１．８×１０＾１５ｃｍ＾－３，这与高性能器件实测值的倒推数值一致，这被解释为长江红外高背景辐射的结果。     

Chromatic luminance,colorimetric purity,and optimal aperture‐color stimuli

Chromatic luminance (i.e., luminance of a monochromatic color) is the source of all luminance, since achromatic luminance arises only from mixing colors and their chromatic luminances. The ratio of chromatic luminance to total luminance (i.e., chromatic plus achromatic luminance) is known as colorimetric purity, and its measurement has long been problematic for nonspectral hues. Colorimetric purity (p_c) is a luminance metric in contrast to excitation purity, which is a chromaticity‐diagram metric approximating saturation. The CIE definition of p_c contains a fallacy. CIE defines maximum (1.0) p_c for spectral stimuli as monochromatic (i.e., optimal) stimuli, and as the line between spectrum ends for nonspectrals. However, this line has <0.003 lm/W according to CIE colorimetric data and is therefore effectively invisible. It only represents the limit of theoretically attainable colors, and is of no practical use in color reproduction or color appearance. Required is a locus giving optimal rather than invisible nonspectral stimuli. The problem is partly semantic. CIE wisely adopted the term colorimetric purity, rather than the original spectral luminance purity, to permit an equivalent metric for spectrals and nonspectrals, but the parameter of equivalence was never clear. Since 1 p_c denotes optimal aperture‐color stimuli for spectrals, arguably 1 p_c should denote optimal stimuli consistently for all stimuli. The problem reduces to calculating optimal aperture‐color stimuli (“optimal” in energy efficiency in color‐matching) for nonspectrals, shown to comprise 442 + 613 nm in all CIE illuminants. This remedy merely requires redefinition of 1 p_c for nonspectrals as the line 442–613 nm, and gives meaningful p_c values over the hue cycle allowing new research of chromatic luminance relations with color appearance. © 2007 Wiley Periodicals, Inc. Col Res Appl, 32, 469–476, 2007     

基于整体特征的快速英文字母识别方法

文章提出了一种基于整体特征的小写英文字母识别方法.首先根据字母图像的赋值背景提取其整体特征,然后构建7个模板进行模板匹配.该方法不需要对图像作复杂的细化处理、轮廓提取等,减少了可能带来的误识和拒识,也不需要现有神经网络方法的长期训练,因而简单快速.同时,不同字体的字母图像其整体特性基本相同,因此识别率较高.     

复杂场景实时目标检测方法

针对含有非平稳背景的复杂场景,提出一种基于空间混合高斯模型的实时目标检测方法.该方法以混合高斯模型作为颜色分布的统计框架,并在空间邻域中利用背景与前景隶属度之间的竞争确定像素的归属,提高了检测准确率;同时通过基于偏差均值的匹配判断和低权重模型的移除提升了混合高斯统计框架的性能,以实时地对运动目标进行准确的检测.实验结果表明,文中方法对非平稳背景有很好的适应能力,在检测准确率和运行效率上均优于其他检测方法.     

智能视频监控中运动目标检测的研究

针对某武器试验中背景复杂,现有的背景差分法在背景模型的维持和更新不能用于长期和复杂的场景,以及对近地目标提取检测困难的问题,提出一种改进的背景差分法。该算法采用结合邻域信息的背景差分法和最大方差阈值法,能够在一定程度上减小背景滞后更新引起的运动目标检测误差,且使目标边缘提取更加明确,从而提高了系统的运行速度,实现复杂背景下的运动目标检测。在Visual C＋＋6.0中用OpenCV实现了相关算法的设计,并给出了完成系统任务所需的部分关键代码,实现了运动目标和试验场景的分离与提取。仿真实验验证了该算法的有效性以及实时性。