Fusion of visible and infrared imagery for face recognition

In recent years face recognition has received substantial attention, but still remained very challenging in real applications. Despite the variety of approaches and tools studied, face recognition is not accurate or robust enough to be used in uncontrolled environments. Infrared (IR) imagery of human faces offers a promising alternative to visible imagery, however, IR has its own limitations. In this paper, a scheme to fuse information from the two modalities is proposed. The scheme is based on eigenfaces and probabilistic neural network (PNN), using fuzzy integral to fuse the objective evidence supplied by each modality. Recognition rate is used to evaluate the fusion scheme. Experimental results show that the scheme improves recognition performance substantially.     

Color fusion of SAR and FLIR images using a natural color transfer technique

Fusion of synthetic aperture radar (SAR) and forward looking infrared (FLIR) images is an important subject for aerospace and sensor surveillance. This paper presents a scheme to achieve a natural color image based on the contours feature of SAR and the target region feature of FLIR so that the overall scene recognition and situational awareness can be improved. The SAR and FLIR images are first decomposed into steerable pyramids, and the contour maps in the SAR image and the region maps in the FLIR image are calculated. The contour and region features are fused at each level of the steerable pyramids. A color image is then formed by transferring daytime color to the monochromic image by using the natural color transfer technique. Experimental results show that the proposed method is effective in providing a color fusion of SAR and FLIR images.     

Transfer color to night vision images

Night vision technology enables human beings to operat at night. Low light level imaging and infrared imaging are two key technologies in night vision field. However the images obtained through the night vision imaging systems are monochromatic, this hinders observers from interpreting the scene well. In order to improve targe detection and identification abilities, color night vision technology is being pursued as an effective method fo solving the problem. The main methods of realizing color…     

基于多视角融合的夜间无人车三维目标检测

为了提高无人车在夜间情况下对周围环境的物体识别能力，提出一种基于多视角通道融合网络的无人车夜间三维目标检测方法。引入多传感器融合的思想，在红外图像的基础上加入激光雷达点云进行目标检测。通过对激光雷达点云进行编码变换成鸟瞰图形式和前视图形式，与红外图像组成多视角通道，各通道信息之间融合互补，从而提高夜间无人车对周围物体的识别能力。该网络将红外图像与激光雷达点云作为网络的输入，网络通过特征提取层、候选区域层和通道融合层准确地回归检测出目标的位置以及所属的类别。实验结果表明，该方法能够提高无人车在夜间的物体识别能力，在实验室的测试数据中准确率达到90%，速度0.43 s/帧，达到了实际应用要求。     

Pixel-clarity-based multifocus image fusion

Due to the limited depth-of-field of optical lenses,it is difficult to get an image with all objects in focus.One way to overcome this problem is to take several images with different focus points and combine theminto a single composite which contains all the regions full focused.This paper describes a pixel-clarity-based multifocus image fusion algorithm.The characteristic of this approach is that the pixels of the fusedimage are selected from the clearest pixels in the input images according to pixel clarity criteria.For eachpixel in the source images,the pixel clarity is calculated.The fusion procedure is performed by a selectionmode according to the magnitude of pixel clarity.Consistency verification is performed on the selectedpixels.Experiments show that the proposed algorithm works well in multifocus image fusion.     

Image fusion based on expectation maximization algorithm and steerable pyramid

In this paper, a novel image fusion method based on the expectation maximization (EM) algorithm and steerable pyramid is proposed. The registered images are first decomposed by using steerable pyramid.The EM algorithm is used to fuse the image components in the low frequency band. The selection method involving the informative importance measure is applied to those in the high frequency band. The final fused image is then computed by taking the inverse transform on the composite coefficient representations.Experimental results show that the proposed method outperforms conventional image fusion methods.     

基于伪彩色融合图像聚类的夜视图像上色算法

提出一种将小波融合和基于伪彩色融合图像的C均值聚类用于图像色彩传递算法中的彩色夜视方法.在色彩传递前将可见光和红外图像进行小波融合得到灰度融合图像作为目标图像,保持了较好的纹理信息和目标信息;再对彩色源网像进行基于连接相对熵的彩色阈值分割;然后针对灰度融合图像的特点,根据一种基于伪彩色融合图像的C均值聚类方法,将伪彩色融合图像的彩色信息作为特征向量应用在夜视图像的分类当中,得到较好的分类效果,并基于此分类结果再进行色彩传递,得到更为自然的彩色夜视图像.实现了对夜视图像的自动色彩传递,得到的罔像色彩较真实.纹理清晰,将有利于人眼的目标识别.     

基于深度学习的无人车夜视图像语义分割

为了增强无人车对夜视图像的场景理解,在夜间模式下更快更精确地探测和识别周围环境,将深度学习应用于夜视图像的场景语义分割,提出了一种基于卷积-反卷积神经网络的无人车夜视图像语义分割方法。在传统的卷积神经网络中加入反卷积网络,构建卷积-反卷积神经网络,无需手工选取特征。通过像素到像素的学习和训练,得到图像语义分割模型,可直接用该模型预测夜视图像中每个像素所属的场景语义类别,实现无人车夜间行驶时的环境感知。实验结果表明,该方法具有较好的准确性和实时性,平均IU达到68.47。     

Multi-sensor image fusion using discrete wavelet frame transform

An algorithm is presented for multi-sensor image fusion using discrete wavelet frame transform (DWFT). The source images to be fused are firstly decomposed by DWFT. The fusion process is the combining of the source coefficients. Before the image fusion process, image segmentation is performed on each source image in order to obtain the region representation of each source image. For each source image, the salience of each region in its region representation is calculated. By overlapping all these region representations of all the source images, we produce a shared region representation to label all the input images. The fusion process is guided by these region representations. Region match measure of the source images is calculated for each region in the shared region representation. When fusing the similar regions, weighted averaging mode is performed; otherwise selection mode is performed. Experimental results using real data show that the proposed algorithm outperforms the traditional pyramid transfo