一种高动态范围图像可视化算法*

提出的自适应HDR图像可视化算法中,输入图像被分解为基本层和细节层.该算法将整体明暗效果的显示看做整体问题,对表示亮度的基本层采用基于整体统计信息的直方图调整算法处理;可视细节信息的保持作为局部问题,算法采用自适应细节增强算法处理细节层.通过定义映射图对细节增强后的图像进行最终映射,将两方面结合起来得到最终结果.实验结果表明,该算法能对HDR图像进行较高视觉质量的显示.     

面向头肩序列图像的质量可精细扩展视频编码方法

FGS编码方法具有细粒度的可扩展能力,能很好地适应网络带宽的动态变化,被认为是一种适合于网络视频传输的编码方案.但现有的MPEG-4 FGS编码标准效率低,限制了其进一步的推广应用.因此,本文面向视频应用中常见的头肩序列图像,实现了一种质量可精细扩展的视频编码方法.该方法采用H.26L对基本层进行编码,采用基于DCT变换的SPIHT方法对原始图像与基本层重建图像之间的残差进行编码得到增强层的码流.然后将复杂背景下的人脸检测与跟踪技术与选择性增强技术结合起来,对人脸区域优先编码.实验结果表明,该方法不仅编码效率高于现有的MPEG-4 FGS标准,码流具有可精细扩展的特性,还可以选择性地提高人脸区域重建图像的主观感受水平.     

Face recognition using illuminant locality preserving projections

A novel supervised manifold learning method was proposed to realize high-accuracy face recognition under varying illuminant conditions.The proposed method,named illuminant locality preserving projections(ILPP),exploited illuminant directions to alleviate the effect of illumination variations on face recognition.The face images were first projected into low-dimensional subspace.Then the ILPP translated the face images along specific direction to reduce lighting variations in the face.The ILPP reduced the distance between face images of the same class,while increase the distance between face images of different classes.This proposed method was derived from the locality preserving projections(LPP)methods,and was designed to handle face images with various illuminations.It preserved the face image’s local structure in low-dimensional subspace.The ILPP method was compared with LPP and discriminant locality preserving projections(DLPP),based on the YaleB face database.Experimental results showed the effectiveness of the proposed algorithm on the face recognition with various illuminations.