Rough membership function based illumination classifier for illumination invariant face recognition

This paper proposes a face recognition system to overcome the problem due to illumination variation. The propose system first classifies the image's illumination into dark, normal or shadow and then based on the illumination type; an appropriate technique is applied for illumination normalization. Propose system ensures that there is no loss of features from the image due to a proper selection of illumination normalization technique for illumination compensation. Moreover, it also saves the processing time for illumination normalization process when an image is classified as normal. This makes the approach computationally efficient. Rough Set Theory is used to build rmf illumination classifier for illumination classification. The results obtained as high as 96% in terms of accuracy of correct classification of images as dark, normal or shadow.     

基于投影的文档图像倾斜校正方法

针对文档图像的倾斜校正问题,提出了一种新的基于投影的文档图像倾斜角检测方法。首先采用一种高效的像素遍历算法对文档图像从不同角度进行投影,然后对投影数据进行累加求和,通过比较不同角度下的累加和来确定倾斜角度。该方法在投影过程中只需对文档图像进行极少部分投影,因而大大减少了运算量。基于该方法的特点,提出了由“粗”到“精”的投影策略,在确保检测精度的同时大幅提高了检测速度。实验结果表明,方法非常有效,可以获得很高的检测精度。     

复杂地形下自动提取多暗像元的TM影像大气纠正方法

复杂地形条件下气溶胶的空间分布变化较大,用单一的气溶胶实测参数或单一的暗像元进行大气纠正都难以获得好的效果,手动选取暗像元还受限于专业人员的经验。通过程序自动提取图像中的浓密植被像元以及位于山区阴影的植被像元作为暗像元,使得暗像元均匀分布于图像的各个区域、各个海拔,更好地模拟复杂地形下的大气状况。根据辐射传输模型,利用迭代、插值等方法获取TM1和TM3波段光学厚度,进而推算成像时刻的气溶胶波长指数及混浊度系数,从而得到各个波段的大气光学厚度以实现大气纠正。     

一种多视角人脸检测方法

目前的人脸检测方法多是针对正面人脸,而对于多视角人脸检测还存在很大困难,有效的方法还不多。本文考虑到人脸检测中旋转人脸和侧面人脸两种多视角情况,提出了一种多视角人脸检测方法。针对平面内旋转的问题,在YCbCr色彩空间内建立肤色模型,经过处理确定人脸椭圆区域,利用基于灰度加权的主成分分析算法进行人脸的角度校正,得到偏转校正后的人脸图像。针对侧面人脸的问题,通过上下和左右2个方向的人脸旋转样本库来训练分类器,然后组合成并联分类器,再对偏转校正后的人脸图像进行人脸验证。实验结果表明,该方法可以对任意视角的人脸进行有效的检测,且有较高的检测率。     

基于氧化锆传感器的氧含量校正及接口电路设计

通过对氧含量测量的现实意义的探讨,以及目前应用较为广泛的氧化锆传感器相关原理的研究,介绍了一种对由氧化锆传感器测得的电压信号进行一系列数据处理,除去误差影响,计算出所需要的待测气体准确的氧体积含量步骤的方法,最后给出传感器与MCU接口电路的硬件设计。     

高压送电线路飞鸟光电探测方法

高压送电线路的鸟害事故已成为送电线路安全运行的一大隐患,为此提出一种用于高压送电线路驱鸟器的飞鸟光电探测方法.采用光电二极管阵列对飞鸟目标进行探测,应用相关技术对信号进行处理,理论分析和实验结果表明,所介绍的飞鸟光电探测方法可探测横向飞行角度(与探测平面夹角)≤60°、距离15 m内的飞鸟目标,同时对背景光线变化不敏感,抗干扰能力较强.     

Stereo Light Probe

In this paper we present a practical, simple and robust method to acquire the spatially‐varying illumination of a real‐world scene. The basic idea of the proposed method is to acquire the radiance distribution of the scene using high‐dynamic range images of two reflective balls. The use of two light probes instead of a single one allows to estimate, not only the direction and intensity of the light sources, but also the actual position in space of the light sources. To robustly achieve this goal we first rectify the two input spherical images, then, using a region‐based stereo matching algorithm, we establish correspondences and compute the position of each light. The radiance distribution so obtained can be used for augmented reality applications, photo‐realistic rendering and accurate reflectance properties estimation. The accuracy and the effectiveness of the method have been tested by measuring the computed light position and rendering synthetic version of a real object in the same scene. The comparison with standard method that uses a simple spherical lighting environment is also shown.     

The Visual Computing of Projector‐Camera Systems

This article focuses on real‐time image correction techniques that enable projector‐camera systems to display images onto screens that are not optimized for projections, such as geometrically complex, coloured and textured surfaces. It reviews hardware‐accelerated methods like pixel‐precise geometric warping, radiometric compensation, multi‐focal projection and the correction of general light modulation effects. Online and offline calibration as well as invisible coding methods are explained. Novel attempts in super‐resolution, high‐dynamic range and high‐speed projection are discussed. These techniques open a variety of new applications for projection displays. Some of them will also be presented in this report.     

Spectral preconditioning of Krylov spaces: Combining PLS and PC regression

In regularized regression the vectors that lie in Krylov and eigen subspaces, formed in PLS and PC regressions respectively, provide useful low dimensional approximations for the LS regression coefficient vector. By preconditioning the LS normal equations we provide a framework in which to combine these methods, and so exploit both of their respective advantages. The link between the proposed method to orthogonal signal correction and to cyclic subspace regression is made. The performance of the proposed solution in reducing the dimension of the regression problem, and the shrinkage properties of the resulting coefficient vector, are both examined.