基于Sigmoid函数局部视觉适应模型的真实影像再现

为解决图像采集与显示设备之间的动态范围差异,色调映射技术试图建立一种由高到低的动态范围映射关系,可用于一般图像的真实影像再现.在不同亮度适应水平下,人眼能产生不同的响应特性及对比度敏感性,从而同时响应不同明暗的光强.由此建立了一种局部视觉适应的再现算法,采用具有参数控制的Sigmoid函数来模拟视觉适应的S形非线性特点,得到不同局部亮度适应水平下的压缩曲线,因此能较好地协调图像整体对比度、亮度调整与局部增强之间的关系,在增强图像较暗区域的同时,极大程度地保持亮区细节.经主观评价与特征统计参数相结合的方法验证,该算法能有效地实现动态范围压缩,保持图像细节而避免伪像,具有一定的色彩恒定性,且复杂度低,具有很好的实用性.     

Evaluation of the acoustic performance of classrooms in public schools

This paper presents the results of an evaluation of acoustic comfort of classrooms built according to a standard design. Three constructive designs located in the metropolitan area of Curitiba (Brazil) have been evaluated, two schools built under each of these three designs, in a total of six schools. The acoustic quality of the classrooms have been analyzed based on measurements of the reverberation time, sound pressure level inside and outside the classrooms, and sound insulation. Measurements of ambient noise (external and internal) followed the Brazilian Standards NBR 10151 and NBR 10152. Measurement of reverberation time and sound insulation followed the international Standards ISO 140-4, ISO 140-5, ISO 717-1, and ISO 3382. Results (sound insulation and reverberation time) have been compared with reference values found in the Brazilian Standard NBR 1279, and in the Standards ANSI S12.60 and DIN 18041. Results reveal poor acoustical quality of the surveyed classrooms, for all 3 constructive designs studied. The surveyed designs do not meet the guidelines of either the Brazilian Standards or of the International Standards employed as references.     

基于图像融合的动态轮廓线跟踪新方法

红外与可见光传感器是目标跟踪识别系统中常用的两种传感器,对这两种传感器图像进行融合能有效提高系统跟踪检测的准确性。将动态轮廓线模型与图像融合结合,在特征搜索过程中利用特征点准确地完成了图像配准,同时使用了一种新的特征级融合方法,将两种图像中目标轮廓的B样条曲线控制点进行实时微分耦合。这种耦合将Curwen提出的微分耦合机制作了改进,利用图像配准把刚性硬模板改变为实时的变换模板并推导了融合后动态轮廓线的新的动力学方程。这种融合利用了红外图像目标轮廓信息约束可见光图像中动态轮廓线的收敛形状,有效地提高了可见光图像目标跟踪的准确性。对运动人手序列图像的对比跟踪实验表明,这种融合使得可见光图像中动态轮廓线平均跟踪误差减小了60．25％。     

基于神经元电响应的人体热舒适性评价理论

本文提出一种利用神经元电响应频率定量评价人体热舒适性的理论方法和准则。利用Hodgkin-Huxley方程和Pennes生物传热方程描述皮下神经元在瞬态温度场下的电响应特性,并研究了环境温度、对流换热系数,血液灌注率和组织导热率等参数对它的影响,结果表明神经元动作电位频率在等效温度发生0.1℃时就会发生明显变化,可以组合多个环境和热物性参数评价人体热舒适性。     

基于标志点识别的自主车视觉导航

提出了一种基于室内标志点识别的单目视觉导航的方法。在地面上有规律地旋转一些标志点,采用辅助先验知识对标志点进行识别和分割,利用透视投影变换得到标志点与摄像机之间的相对距离,达到通过单目摄像机获得景物深度信息的目的。整个算法结构简单,易于实现,对由照明不均匀和地面反光所引起的各种干扰有较好地抑制作用。     

基于银纳米粒子的生成测定痕量甲醛

在碱性溶液中甲醛能还原Ag~+得到黄色银纳米粒子,使体系的共振光散射(RLS)强度增强,从而建立起测量环境中痕量甲醛的RLS新方法. 结果表明,新建方法测定甲醛的浓度线性范围为1.0×10~(-6)～2.0×10~(-5) mol/L,检出限为1.0×10~(-7) mol/L,样品加标测定的回收率为96.26%～103.32%. 并且不同浓度的甲醛还原Ag~+得到黄色银纳米粒子的颜色明显不同,基于此建立了一种可视化半定量测定痕量甲醛的新方法,此方法简便快速、灵敏度高. 用于环境水样、室内空气中甲醛的测定,结果满意.     

基于背景原型对比度的显著性物体检测

针对传统显著性模型在自然图像的显著性物体检测中存在的缺陷,提出了一种利用背景原型(background prototypes)进行对比的视觉关注模型,以实现显著性物体的检测与提取;传统显著性模型主要通过计算区域中心与四周区域差异性实现显著性检测,而自然场景中显著性区域和背景区域往往都存在较大差异,导致在复杂图像中难以获得理想检测效果;基于背景原型对比度的显著性物体检测方法在图像分割生成的超像素图基础上,选择距离图像中心较远的图像区域作为背景原型区域,通过计算图像中任意区域与这些背景原型区域的颜色对比度准确检测和提取图像中的显著性物体;实验结果表明,基于背景原型对比度的显著性模型可以更好地滤除杂乱背景,产生更稳定、准确的显著图,在准确率、召回率和F-measure等关键性能和直观视觉效果上均优于目前最先进的显著性模型,计算复杂度低,利于应用推广。     

多源信息融合的环境舒适度检测与评价

为了能够实现对室内环境的监测和舒适度的调节,首先需要获取准确、及时的室内环境参数。针对目前室内环境监测系统存在自动化程度低、检测点部署欠灵活,安装维护困难,监控难度大等问题,设计开发了一种基于无线传感网络的室内环境监测系统。以ZigBee技术为核心,构建无线传感器网络,自动采集和传输室内相关环境参数;通过结合物联网云平台,实现远程监控和报警;运用模糊综合评价的方法,通过统计、分析、计算,得到符合实际的室内环境舒适度水平。本系统通过硬件设计降低了室内环境监测成本,通过软件设计提高了系统的监测水平和灵活性,通过结合模糊综合评价方法和物联网云平台降低了监控难度。经实验,验证了本系统在实际环境下的使用性能,适合推广应用。     

Image quality assessment in multiband DCT domain based on SSIM

The performance of image quality assessment method based on SSIM (structural similarity) is better than the PSNR (peak signal to noise ratio), but the assessment effects of SSIM is poor for seriously blurred image, therefore, the model that combined HVS (human visual sensitivity) and SSIM was established. The basic idea is based on the human eye's sensitivity to different frequency distortion image, the image is two-dimensional discrete cosine transform frequency component into low, mid, high-frequency component, to obtain the frequency component of light, contrast and structural information, using Pearson coefficient for weight and sum processing to the sub-image according to frequency bands of different sensitive degree, finally, get the sharpness of the image. Through nonlinear regression analysis of objective assessment and DMOS, experiments showed that this method was closer to human perception than SSIM and GSSIM for serious blurred distortion image. At the same time, compared to conventional algorithm MAE (mean absolute error), MSE (mean square error) and PSNR, this model was more consistent with human visual characteristics.