飞行模拟器虚拟仪表设计

阐述某型飞机飞行模拟器仪表仿真系统的设计方案,给出在Visual C++6.0下基于OpenGL的虚拟仪表的绘制方法。实践证明,基于OpenGL的虚拟仪表建模形象逼真,代码的可重用性好、效率高、扩展性强,在通用性和模块化方面具有良好的特性。     

利用SVM与灰度共生矩阵从QuickBird影像中提取枇杷信息

以福建省莆田市东圳水库库区为例,采用QuickBird卫星影像,利用主成分分析方法对灰度共生矩阵方法提取的地物纹理特征进行筛选,选择最佳的影像纹理特征,组成新的波段组合,并应用支持向量机方法(Support Vector Machine,SVM)进行枇杷树的提取分类,最后与只依靠光谱信息来分类的SVM法分类结果进行比较,其分类总精度由原来的71.33%提高到了86.67%,Kappa系数也由原来的0.6410提高到了0.8293,分类精度明显提高,表明光谱信息加入纹理特征信息能辅助并提升高分辨率遥感枇杷树信息提取的精度。     

将局部二进制模式应用于动态纹理识别的新方法

动态纹理的处理、描述与识别是纹理分析的热门领域。动态纹理是对普通纹理在时间域方面的扩展,包括动态特征和静态特征。基于LBP算法的扩展提出的VLBP算法较好的描述了动态纹理特征,但是计算量过大,模板过多。本文提出了一种由VLBP算法改进的基于局部二进制运动模式的特征提取方法用以动态纹理的描述和识别,它包括提取动态特征和提取静态特征两部分。将LBP算子做为块匹配准则提取局部二进制运动模式柱状图做为动态特征的描述,提取LBP柱状图做为静态特征的描述,并将二者连接得出描述动态纹理特征的联合的局部二进制运动模式柱状图。通过对DynTex集实验的结果表明,本文提出的方法在性能和识别率方面均要优于VLBP。     

Augmented Panoramic Video

Many video sequences consist of a locally dynamic background containing moving foreground subjects. In this paper we propose a novel way of re‐displaying these sequences, by giving the user control over a virtual camera frame. Based on video mosaicing, we first compute a static high quality background panorama. After segmenting and removing the foreground subjects from the original video, the remaining elements are merged into a dynamic background panorama, which seamlessly extends the original video footage. We then re‐display this augmented video by warping and cropping the panorama. The virtual camera can have an enlarged field‐of‐view and a controlled camera motion. Our technique is able to process videos with complex camera motions, reconstructing high quality panoramas without parallax artefacts, visible seams or blurring, while retaining repetitive dynamic elements.     

Building Illumination Coherent 3D Models of Large-Scale Outdoor Scenes

Systems for the creation of photorealistic models using range scans and digital photographs are becoming increasingly popular in a wide range of fields, from reverse engineering to cultural heritage preservation. These systems employ a range finder to acquire the geometry information and a digital camera to measure color detail. But bringing together a set of range scans and color images to produce an accurate and usable model is still an area of research with many unsolved problems. In this paper we address the problem of how to build illumination coherent integrated texture maps from images that were taken under different illumination conditions. To achieve this we present two different solutions. The first one is to align all the images to the same illumination, for which we have developed a technique that computes a relighting operator over the area of overlap of a pair of images that we then use to relight the entire image. Our proposed method can handle images with shadows and can effectively remove the shadows from the image, if required. The second technique uses the ratio of two images to factor out the diffuse reflectance of an image from its illumination. We do this without any light measuring device. By computing the actual reflectance we remove from the images any effects of the illumination, allowing us to create new renderings under novel illumination conditions.     

Learning traversability models for autonomous mobile vehicles

Autonomous mobile robots need to adapt their behavior to the terrain over which they drive, and to predict the traversability of the terrain so that they can effectively plan their paths. Such robots usually make use of a set of sensors to investigate the terrain around them and build up an internal representation that enables them to navigate. This paper addresses the question of how to use sensor data to learn properties of the environment and use this knowledge to predict which regions of the environment are traversable. The approach makes use of sensed information from range sensors (stereo or ladar), color cameras, and the vehicle’s navigation sensors. Models of terrain regions are learned from subsets of pixels that are selected by projection into a local occupancy grid. The models include color and texture as well as traversability information obtained from an analysis of the range data associated with the pixels. The models are learned without supervision, deriving their properties from the geometry and the appearance of the scene. The models are used to classify color images and assign traversability costs to regions. The classification does not use the range or position information, but only color images. Traversability determined during the model-building phase is stored in the models. This enables classification of regions beyond the range of stereo or ladar using the information in the color images. The paper describes how the models are constructed and maintained, how they are used to classify image regions, and how the system adapts to changing environments. Examples are shown from the implementation of this algorithm in the DARPA Learning Applied to Ground Robots (LAGR) program, and an evaluation of the algorithm against human-provided ground truth is presented.

RoadCompass: following rural roads with vision + ladar using vanishing point tracking

We present a vision- and ladar-based approach to autonomous driving on rural and desert roads that has been tested extensively in a closed-loop system. The vision component uses Gabor wavelet filters for texture analysis to find ruts and tracks from which the road vanishing point can be inferred via Hough-style voting, yielding a direction estimate for steering control. The ladar component projects detected obstacles along the road direction onto the plane of the front of the vehicle and tracks the 1-D obstacle “gap” presumed due to the road to yield a lateral offset estimate. Several image- and state-based tests to detect failure conditions such as off-road poses (i.e., there is no road to follow) and poor lighting due to sun glare or distracting shadows are also explained. The system’s efficacy is demonstrated with analysis of diverse logged data including from the 2005 DARPA Grand Challenge, as well as tests with full control of a vehicle over 15 km of difficult roads at up to 37 km/h with no waypoints.

基于极坐标Log Gabor小波的纹理分析方法

提出一种基于极坐标Log Gabor小波的纹理分析方法,该方法突破了Gabor小波的带宽限制,仅采用较少的极坐标Log Gabor滤波器,即实现了准确地纹理分割.实验表明,在频谱覆盖相同的情况下,极坐标Log Gabor小波具有较Gabor小波更好的性能和更高的效率;此外,实验中针对多通道滤波方法对最小距离判别方法的改进,也取得了较好的效果,这为基于小波分解的分类问题提供了一个新的思路.     

基于非下采样Contourlet和MRF的纹理图像分割

提出了一种基于非下采样Contourlet变换(NSCT)和马尔科夫随机场(MRF)相结合的纹理图像分割算法.算法包括两个步骤,首先通过NSCT实现对图像纹理特征的提取,并使用模糊C-均值完成对图像的初始分割;然后将初始分割结果用 MRF模型表示,通过贝叶斯置信传播得到图像的最终分割结果.实验结果表明,对于纹理图像,该方法在分割错误率、区域一致性以及边缘的准确性方面都比传统小波变换的方法有了明显的改善.     

应用遗传算法仿真墨扩散轮廓

墨扩散效果的模拟是水墨画仿真的重要工作之一,在分析绘画材料特性和扩散形成机制的基础上,提出一个全新的基于遗传算法的墨扩散仿真模型系统.首先将笔迹和纸张离散化为笔元和纸元;其次借用遗传算法基本概念和原理,对给定的输入笔迹进行轮廓提取作为初始种群,通过种群个体间的选择、重组和变异等遗传活动模拟笔元的扩散过程;最后为了更好地模拟墨扩散方向,使用Ashikhmin算法合成各种宣纸的自然纹理,增强了真实感.实验结果表明,方法可以生成丰富的扩散效果,并且接近真实水墨画.