基于AutoCAD和3DSMAX的虚拟校园三维场景构建研究

当前的虚拟校园三维场景构建方法是利用osgEarth获取GoogleEarth数据,构建虚拟校园三维地图,然后依据本地地形信息与三维场景模型完成对虚拟校园的三维场景构建。该方法在三维场景构建过程中没有考虑到校园后续建设发展,只凭现在所需进行三维场景构建,导致虚拟校园三维场景杂乱,存在虚拟校园三维场景构建顾虑不周的问题。为此,提出一种基于AutoCAD和3DSMAX的虚拟校园三维场景构建方法。该方法首先利用Kinect的RGB深度摄像头获取校园平面数据、影像数据和贴图资料,采用相机阵列合成算法对所得校园图像进行特征提取,然后依据非局部均值滤波方法对获取的校园图像进行去噪操作,最后利用边缘方向和梯度表示中的结构方向与强度,对校园图像外轮廓的纹理特征进行收敛,依据外轮廓纹理特征收敛实现校园图像内轮廓的准确定位,将校园图像与虚拟校园三维场景构建所需图像进行匹配,由此完成对虚拟校园三维场景的构建。实验结果证明,所提方法是虚拟校园三维场景构建领域革命性的突破,提高了学校的招生率,增加了校园文化历史的展现机会,为该领域的研究发展提供了有效依据。     

The use of a consumer grade photo camera in optical-digital correlator for pattern recognition and input scene restoration

In this work an optical-digital correlator for pattern recognition and input scene restoration is described. Main features of the described correlator are portability and ability of multi-element input scenes processing. The correlator consists of a consumer grade digital photo camera with a diffractive optical element (DOE) inserted as a correlation filter. Correlation of an input scene with a reference image recorded on the DOE are provided optically and registered by the digital photo camera for further processing. Using obtained correlation signals and DOE’s point spread function (PSF), one can restore the image of the input scene from the image of correlation signals by digital deconvolution algorithms.The construction of the correlator based on the consumer grade digital photo camera is presented. The software procedure that is necessary for images linearization of correlation signals is described. Experimental results on optical correlation are compared with numerical simulation. The results of images restoration from conventionally and specially processed correlation signals are reported. Quantitative estimations of accuracy of correlation signals as well as restored images of the input scene are presented.     

Omni-total variation algorithm for the restoration of all-focused catadioptric image

In this paper, we present a novel omni-total variation (Omni-TV) algorithm for the restoration of defocus blur to obtain all-focused catadioptric image. Catadioptric omni-directional imaging systems usually consist of conventional cameras and curved mirrors for capturing 360° field of view. Mirror curvature in the catadioptric camera often leads to noticeable blurring artifacts in omni-directional imaging. The problem becomes more severe when high resolution sensor is introduced. In an omni-directional image, two points near each other may not be close to one another in the 3D scene. Traditional gradient computation cannot be directly applied to omni-directional image processing. Thus, an omni-gradient computing method combined with the characteristics of catadioptric imaging is proposed, in which an Omni-TV minimization is used as the constraint for deconvolution regularization. The proposed method is vital for improving catadioptric omni-directional imaging quality and promoting applications in related fields like omni-directional video and image processing.     

Depth information extraction with an aperture-rotating camera

This paper focuses on extracting depth information with an aperture-rotating camera. Compared with previous work, the proposed method is applicable for arbitrary rotation angle by making use of pixel registration to get pixel shift angle, and calculating the pixel shift along this angle. Besides, consumption time for shift angle is reduced by means of sampling. Compared with point-by-point method, the proposed method reduces the computation time from about 1 h to 1 min for a 15-megapixel image. Experimental results of a scene named “bottles” show that the depth value has a margin of error of 5%. Proposed method makes it possible to extract depth information with a single camera and expands the application of DSLR camera.     

Real-time increase in depth of field of an uncooled thermal camera using several phase-mask technologies

Imaging systems that combine a phase mask in the pupil and digital postprocessing may have better performance than conventional ones. We have built such a system to enhance the depth of field of an uncooled thermal camera. The phase masks are binary, their structures are optimized thanks to an image quality criterion, and they have been realized with three different technologies that give equivalent results. The deconvolution postprocessing is performed in real time with a graphics processing unit. A significant increase of the depth of field of a factor 3 has been obtained.     

基于马尔算法的高速公路视频监控图像能见度研究

根据高速公路沿线的监控摄像机,对监控视频画面中的图像进行采集,通过对视频图像特征的分析处理,建立图像与真实场景之间的关系,根据图像特征随着真实场景的变化,运用图像处理的方法如：灰度变换、图像分割和特征提取等对图像进行图像处理,提出运用马尔算法,分别提取出目标物与背景,并将其逐一进行背景差计算,能够准确的监控图像中汽车的位置变化,确定目标物的位置,进而判别出能见度的大小。     

Fast model-based multispectral imaging using nonnegative principal component analysis

Estimation of the spectral reflectance of a scene is a critical problem in image processing and computer vision applications. Model-based multispectral imaging, one of the spectral reflectance estimation methods, can effectively reconstruct the full spectrum using a small number of camera shots. However, it is based on iterative optimization and, thus, is computationally too intensive. In this Letter, we modify the iterative optimization problem to a closed-form problem using nonnegative principal component analysis. The proposed method can substantially reduce the computational cost while maintaining the accuracy.     

基于环境光检测的场景融合系统

基于深度相机和人眼检测模型实时返回观察者眼睛的位置坐标,利用坐标转换模型计算合理的显示区域,通过颜色传感器的颜色分量数据估计环境色温和亮度;根据提前标定的显示参数映射表调整显示状态,使其最接近环境光的色彩表现。搭建了场景融合实验系统,系统分为位置监测相机、图像采集相机、颜色传感器、显示设备、处理器五部分.分别在色温为6354 K,照度为160 lx的室内场景和色温为6197 K,照度为848 lx的室外场景下进行融合实验.实验结果表明,场景融合方案能够为不同位置的观察者调整显示画面,并根据环境光信息改变显示参数,融合效果优良,单次执行仅需283 ms.     

Computer generated hologram from full-parallax 3D image data captured by scanning vertical camera array (Invited Paper)

Full-parallax light-field is captured by a small-scale 3D image scanning system and applied to holographic display. A vertical camera array is scanned horizontally to capture full-parallax imagery, and the vertical views between cameras are interpolated by depth image-based rendering technique. An improved technique for depth estimation reduces the estimation error and high-density light-field is obtained. The captured data is employed for the calculation of computer hologram using ray-sampling plane. This technique enables high-resolution display even in deep 3D scene although a hologram is calculated from ray information, and thus it makes use of the important advantage of holographic 3D display.     

公路场景去雾技术研究

提出了一种应用单目机器视觉技术并结合Hough变换消失线检测和边界检测技术来构建距离深度图的方法,并应用衰减-空气光去雾模型对图像进行了恢复。结果表明,采用该方法恢复的道路图像色彩自然,模型与实际道路完全匹配,对于不同距离的景物都具有良好的恢复效果。     

Real-Time Generation of Dynamic Infrared Scene

Although many models have been put forward to realize static infrared scene, they could not generate dynamic infrared scene real time in interactive way. In this paper a new method is proposed to solve the problem. We first model the targets and background of infrared scene based on the hybrid way of geometry and multi-spectral texture images. Then considering the attenuation effect of atmosphere and the noise mechanic of infrared image sensor, we present an infrared depth image model to generate dynamic images of the objects in the scene from different viewpoint. The complexity of infrared dynamic scene is thus reduced greatly and the reality of infrared scene is improved. Finally, real-time walkthrough for infrared scene is successfully realized and the average walkthrough speed is larger than 25 frames per second.     

Calculating depth maps from digital holograms using stereo disparity

Depth extraction is an important aspect of three-dimensional (3D) image processing with digital holograms and an essential step in extended focus imaging and metrology. All available depth extraction techniques with macroscopic objects are based on variance; however, the effectiveness of this is object dependent. We propose to use disparity between corresponding points in intensity reconstructions to determine depth. Our method requires a single hologram of a scene, from which we reconstruct two different perspectives. In the reconstruction the phase information is not needed, which makes this method useful for in-line digital holography. To our knowledge disparity based 3D image processing has never been proposed before for digital holography.     

浅海被动水下偏振成像探测方法

针对传统被动水下偏振成像方法忽略水体对光的吸收效应,成像结果中存在严重的色彩失真,且并未深入发掘利用背景散射光中包含的场景信息的问题.提出浅海被动水下偏振成像探测方法,该方法从水体中背景散射光的传输特性出发,分析场景深度信息与散射光的物理关系,建立基于深度信息的水下Lambertian反射模型,实现无色彩畸变的水下目标场景清晰成像探测.实验结果表明,该方法能够提供接近水下目标真实色彩、符合人眼视觉特性的清晰探测结果,提高水下成像探测能力.     

Blind motion deblurring using multiple images

Recovery of degraded images due to motion blurring is a challenging problem in digital imaging. Most existing techniques on blind deblurring are not capable of removing complex motion blurring from the blurred images of complex structures. One promising approach is to recover the clear image using multiple images captured for the scene. However, in practice it is observed that such a multi-frame approach can recover a high-quality clear image of the scene only after multiple blurred image frames are accurately aligned during pre-processing, which is a very challenging task even with user interactions. In this paper, by exploring the sparsity of the motion blur kernel and the clear image under certain domains, we propose an alternative iteration approach to simultaneously identify the blur kernels of given blurred images and restore a clear image. Our proposed approach is not only robust to image formation noises, but is also robust to the alignment errors among multiple images. A modified version of linearized Bregman iteration is then developed to efficiently solve the resulting minimization problem. Experiments show that our proposed algorithm is capable of accurately estimating the blur kernels of complex camera motions with minimal requirements on the accuracy of image alignment. As a result, our method is capable of automatically recovering a high-quality clear image from multiple blurred images.     

光子计数激光雷达的时间相关卡尔曼深度估计

在高背景噪声和低积分时间的激光雷达远距离成像场景中,针对传统方法得到的深度图像目标被噪声淹没和深度估计偏差较大的问题,提出了一种基于信号光子时间相关性和自适应卡尔曼滤波器的深度信息估计方法。首先,提取在时间上具有聚集特征的光子计数形成集合;然后,分析了影响信号光子在时间上分布的因素并使用静态高斯线性模型来描述该集合;最后将集合中的所有光子飞行时间乱序,输入改进的自适应卡尔曼滤波器,从而迭代估计深度值。在信号噪声比为1的室内,积分时间分别为10 ms和1 ms时,本文方法相对传统的最大似然方法在均方根误差指标上提升了40%和38%。在信噪比约为0.135的室外2 km目标成像实验中,在信号光子数分别为100、33和17的情况下,本文方法成像效果都优于传统最大似然估计方法和时间相关光子快速去噪方法,得到的深度图像都更清晰,噪声更低。在高噪声和短积分时间下,本文方法可以被运用于激光雷达远距离成像的深度信息估计和图像恢复中。     

微光景像匹配实时图模拟生成技术研究

景像匹配实时图模拟生成是导弹制导系统仿真和景像匹配算法验证的关键技术。研究了光照条件、气象条件、像增强型电荷耦合器(ICCD)相机特性以及地面光谱反射特性等因素对微光成像的影响,建立了微光视景像生成的数学模型,包括:地面反射特性模型、像增强型电荷耦合器相机特性模型、大气传输模型和噪声模型,研究了白天与夜间地面反射特性造成的日光图像与微光图像的衬比度差异,提出了微光景像匹配实时图仿真方案,并在此基础上研制了一个完整的蕴含LOWTRAN7计算内核和像增强型电荷耦合器后段仿真的微光景像匹配实时图生成系统。仿真实验结果说明了该方案的有效性。     

联合梯度预测与导引滤波的图像运动模糊复原

针对由相机与所摄景物之间发生相对位置移动所导致的图像运动模糊,提出了一种鲁棒的基于单幅运动模糊图像的盲反卷积算法。该方法首先通过预测图像中的较强边缘信息,实现用简单、易于求解的优化问题在傅里叶域中快速、准确地估计出点扩散函数。然后利用得到的点扩散函数,使用基于梯度约束的非盲反卷积算法复原清晰图像,同时采用一种新的边缘保持滤波器-导引滤波来消除噪声并抑制振铃效应。实验结果表明：本文的算法能够快速地从单幅运动模糊图像复原出具有清晰边缘和纹理的高质量图像,并且运算时间不超过20 s。     

机器视觉疲劳裂纹扩展试验摄像头自动聚焦方法

在机器视觉疲劳裂纹扩展试验中,为了能够满足对裂纹宏观观察裂纹和准确定位裂纹尖端的要求,需要采用变焦镜头放大和缩小采集图像范围,针对于摄像头变焦后的聚焦问题,提出了摄像头的自动聚焦方法。首先建立图像采集系统,采集裂纹图像至计算机,通过中值滤波对图像预处理,去除噪声干扰,选取裂纹区域作为聚焦窗口,采用Laplace算子法作为清晰度评价函数,并提出一种变步长穷举法进行聚焦搜索。最后设计了以ARM7为核心的摄像头运动控制器。实验表明,所提出的方法能够实现疲劳裂纹扩展试验中摄像头在各种情况下的准确自动聚焦,为下一步精确测量疲劳裂纹扩展参数奠定基础。     

Blind image deconvolution using sparse and redundant representation

This work devotes to the image deconvolution problem that restores clear image from its blurred and noisy measurements with little prior about the blur. A deconvolution method based on sparse and redundant representation theory is developed in this paper. It firstly represents the blur and image over different redundant dictionaries and imposes sparsity constraint to their representation coefficients respectively, then alternately estimates them using an iterative algorithm employing optimization technique. Experimental results on astronomical images show that the proposed method can achieve as good performance as the method requiring a known blur, which demonstrates its effectiveness.     

Image restoration for interlaced scan CCD image with space-variant motion blurs

When the speeds of objects in a scene exceed the temporal resolution of the camera shutter, motion blurs will occur. Since objects are often moving in different directions at different speeds, the degradation of a CCD image is often characterized by space-variant motion blurs. Image restoration algorithms for space-variant motion blurs are available for progressive scan CCD images, but not for interlaced scan images. To address the space-variant image restoration for interlaced scan images, a novel three-step image restoration scheme is proposed. Firstly, one interlaced scan image is divided into odd field and even field images. Secondly, these two field images are further segmented into rectangular blocks and the motion vectors are computed based on these rectangular blocks using an efficient block matching algorithm. Thirdly, image restoration is performed using a blind deconvolution algorithm in the odd or even field image. The final restored image is obtained by combining the restored odd and even field images. The scheme is illustrated by restoring a space-variant blurred moving vehicle image and a synthetic blurred image.