基于自适应分块的移动设备上特征点提取方法

当前,移动设备的计算能力越来越强,但类似增强现实对实时性要求较高的应用,其性能还无法完全满足需求。在增强现实应用中,提取特征点和特征点的匹配是关键。主要关注特征点的提取,以SURF算法为例,针对移动设备较小的高速缓存容量不适应原算法图像数据访问方式的问题,提出了依据图像内容的自适应分块方法。实验结果表明,改进后的算法在没有牺牲精确度的情况下,速度提高了1.5¹.7倍。     

基于Android移动设备的增强现实技术研究

针对移动设备弱处理能力和低内存等局限性,导致增强现实技术不能在移动设备上普及,提出一种新的增强现实技术方案。采用SURF算法提取视频帧图像的特征点,接着使用FREAK算法进行特征点描述;在汉明距离强制匹配之后,采用改进的RANSAC算法剔除了误匹配点,然后通过计算比较匹配特征点占样本图像特征点的比重,判断是否成功匹配目标物体;最后渲染对应的三维模型完成增强现实三维注册。通过在Android移动设备上的验证,结果表明该技术方案满足移动设备对实时性、准确性和鲁棒性的要求,为移动增强现实的推广奠定了良好的基础。     

移动增强现实中视觉三维注册方法的实现

为了提高移动增强现实注册技术的性能,采用了一种基于计算机视觉的注册算法。该方法利用ARToolkit中的标识检测法查找相关的标识和特征点,然后基于视觉三维注册算法得到模型视图矩阵,最后在相应位置绘制虚拟物体。实验结果表明这种注册方法在实时性、稳定性、抗遮挡性方面效果良好,满足移动增强现实系统高精度三维注册的要求。     

基于最小边界扇形的移动对象轨迹实时化简算法

为了对全球定位系统(GPS)设备采集到的移动对象原始轨迹数据进行简化,提高轨迹数据的使用效率,降低移动终端的通信代价和计算开销,提出了一种基于最小边界扇形(MBS)的移动对象轨迹实时化简算法。该算法不同于用一条折线来近似原始轨迹的方法,它利用扇形预测范围来估计、简化原始轨迹,在角度和距离两个层面上对简化误差进行控制。提出了新的误差度量方法--基于等极径的误差度量方法,并讨论了GPS定位误差对简化算法的影响。实验结果表明,所提算法的简化轨迹高效、稳定,所得到的简化轨迹与原始轨迹之间误差较小(不超过误差阈值的20%),对GPS定位误差有较好的容错能力。     

基于井下移动图像采集的目标识别与精确定位方法

针对现有井下定位方法定位精度波动较大、难以进一步提高的问题,提出一种基于井下移动图像采集的目标识别与精确定位方法。利用定位目标携带的摄像机采集环境图像,通过自适应直方图均衡化方法对采集到的原始图像进行预处理,采用深度学习技术SSD算法、数据增强SSD算法识别井下标志目标,并采用基于小孔成像原理的单目测距方法进行测距和定位。实验结果表明:与灰度图像匹配算法和特征图像匹配算法2种传统算法相比,SSD算法对距离和角度变化的适应能力更好,距离为4.5m时有效检测率仍达89.2%;数据增强SSD算法提高了鲁棒性,检测精确率比SSD算法高1.7%,可以更好地适应复杂环境。井下应用结果表明,基于井下移动图像采集的目标识别与精确定位方法在2～10m范围内可得到较理想的效果,随着距离增加,测量精度有所下降。     

结合图像识别的移动增强现实系统设计与应用

目的 在移动互联网时代下,移动增强现实应用得到越来越快的发展。然而户外场景中存在许多相似结构的建筑,且手机的存储和计算能力有限,因此应用多集中于室内小范围环境,对于室外大规模复杂场景的适应性较弱。对此,建立一套基于云端图像识别的移动增强现实系统。方法 为解决相似特征的误匹配问题,算法中将重力信息加入到SURF和BRISK特征描述中去,构建Gravity-SURF和Gravity-BRISK特征描述。云端系统对增强信息进行有效管理,采用基于Gravity-SURF特征的VLAD方法对大规模图像进行识别;在智能终端上的应用中呈现识别图像的增强信息,并利用识别图像的Gravity-BRISK特征和光流结合的方法对相机进行跟踪,采用Unity3D渲染引擎实时绘制3维模型。结果 在包含重力信息的4 000幅户外图像的数据库中进行实验。采用结合重力信息的特征描述算法,能够增强具有相似特征的描述符的区分性,并提高匹配正确率。图像识别算法的识别率能达到88%以上,识别时间在420 ms左右;光流跟踪的RMS误差小于1.2像素,帧率能达到23 帧/s。结论 本文针对室外大规模复杂场景建立的基于图像识别的移动增强现实系统,能方便对不同应用的增强现实数据进行管理。系统被应用到谷歌眼镜和新闻领域上,不局限于单一的应用领域。结果表明,识别算法和跟踪注册算法能够满足系统的精度和实时性要求。     

基于ORB和KLT的移动增强现实三维注册算法

针对移动设备上现有注册算法速度较慢和实时性较弱的问题,提出一种基于ORB和KLT的移动增强现实三维注册算法,利用ORB进行特征提取和匹配,减少算法时间开销,同时使用改进的KLT算法对特征位置进行预测跟踪,改善移动三维注册算法中的实时性能。实验结果表明,在移动设备上采用ORB和KLT算法,不仅较好地解决移动增强现实三维注册实时性差的问题,同时也能保证有效的匹配性能。     

基于感知哈希和视觉词袋模型的图像检索方法

针对移动增强现实中图像检索技术耗时长导致的实时性不高的问题,提出了一种 基于感知哈希和视觉词袋模型结合的图像检索方法。图像检索过程中,在保证一定正确率的基 础上加快了检索速度。首先,对数据集图像使用改进的感知哈希技术处理,选取与查询相似的 图像集合,达到筛选图像数据集的作用;然后,对相似图像集使用视觉词袋模型进行图像检索, 选取和查询图像中目标一致的目标图像。实验结果表明,该方法相比较视觉词袋模型算法检索 的平均正确率提高了 3.2%,检索时间缩短了 102.9 ms,能够满足移动增强现实中图像检索的实 时性要求,为移动增强现实系统提供了有利的条件。     

基于平衡距离的无线传感器网络节点部署算法

针对随机部署的无线传感器节点,提出一种节点移动方案。将节点移动划分成若干个过程进行,在每个移动过程中根据平衡距离和位置关系进行节点移动,使聚集在一起的节点分散开,实现对监测区域的最大覆盖。由于节点间平衡距离逐渐增大,因此在每个移动过程中节点的移动距离均较小,减少了节点移动距离总和。仿真结果表明,该算法在保证覆盖效果的基础上,降低节点的总移动距离。     

一种无记忆点云迭代简化算法

针对目前点云迭代简化算法在简化过程中需要保持原始模型信息而占用较大内存的问题,提出了一种无记忆点云迭代简化算法,简化过程中不需要记录原始模型相关几何信息。该算法使用体积优化和距离优化计算点对收缩后的最优点位置并对点对进行排序。实验表明,该算法可以在内存占用较小的情况下得到误差较小的简化模型。     

A Geometry-Based Point Cloud Reduction Method for Mobile Augmented Reality System

In this paper, a geometry-based point cloud reduction method is proposed, and a real-time mobile augmented reality system is explored for applications in urban environments. We formulate a new objective function which combines the point reconstruction errors and constraints on spatial point distribution. Based on this formulation, a mixed integer programming scheme is utilized to solve the points reduction problem. The mobile augmented reality system explored in this paper is composed of the offline and online stages. At the offline stage, we build up the localization database using structure from motion and compress the point cloud by the proposed point cloud reduction method. While at the online stage, we compute the camera pose in real time by combining an image-based localization algorithm and a continuous pose tracking algorithm. Experimental results on benchmark and real data show that compared with the existing methods, this geometry-based point cloud reduction method selects a point cloud subset which helps the image-based localization method to achieve higher success rate. Also, the experiments conducted on a mobile platform show that the reduced point cloud not only reduces the time consuming for initialization and re-initialization, but also makes the memory footprint small, resulting a scalable and real-time mobile augmented reality system.     

基于Android平台的增强现实算法

为了获得更加理想的增强现实效果,提出一种基于利用 Android 平台的增强现实算法。首先采用 FAST算法确定两幅图像中的特征角点,并采用SURF生成特征点描述符,然后采用快速近似邻近点搜索进行图像匹配,最后在Android平台上实现算法,并采用仿真实验测试算法的性能。仿真结果表明,本文算法结合Android的优点,可以较好的满足增强现实的实时性的要求,而且具有较好的鲁棒性,克服了传统增强现实技术的局限性。     

基于巡逻无人机的轻量型安全帽佩戴检测方法与应用

目前,安全帽检测系统主要使用固定摄像头,无法实现全区域检测,而基于深度学习的检测算法结构复杂、计算成本高,无法满足移动端和嵌入式设备的部署要求。针对上述问题,该文提出一种基于无人机的安全帽轻量型视觉检测算法。系统通过无人机平台搭载的相机对施工现场进行图像采集,并无线传输至后台计算机进行处理,检测算法基于 YOLOv5s 框架进行了轻量化改进。针对无人机采集影像中目标占比较小的问题,该文采用了多尺度检测、图像预处理、正负样本不均衡等方法,对 YOLOv5s 目标检测算法进行针对性改进。测试结果表明,与原模型相比,轻量型目标检测模型的平均精度均值仅下降了 1.72%,但在同一 CPU 上的推理速度提升了 1 倍,浮点计算量由原来的每秒 165 亿次压缩至每秒 34 亿次,模型大小约为原模型的 1/10。     

A Tracking Registration Method for Augmented Reality Based on Multi-modal Template Matching and Point Clouds

In order to overcome the defects where the surface of the object lacks sufficient texture features and the algorithm cannot meet the real-time requirements of augmented reality, a markerless augmented reality tracking registration method based on multimodal template matching and point clouds is proposed. The method first adapts the linear parallel multi-modal LineMod template matching method with scale invariance to identify the texture-less target and obtain the reference image as the key frame that is most similar to the current perspective. Then, we can obtain the initial pose of the camera and solve the problem of re-initialization because of tracking registration interruption. A point cloud-based method is used to calculate the precise pose of the camera in real time. In order to solve the problem that the traditional iterative closest point (ICP) algorithm cannot meet the real-time requirements of the system, Kd-tree (k-dimensional tree) is used under the graphics processing unit (GPU) to replace the part of finding the nearest points in the original ICP algorithm to improve the speed of tracking registration. At the same time, the random sample consensus (RANSAC) algorithm is used to remove the error point pairs to improve the accuracy of the algorithm. The results show that the proposed tracking registration method has good real-time performance and robustness.

     

Improving performance on object recognition for real-time on mobile devices

Augmented reality has been on the rise due to the proliferation of mobile devices. At the same time, object recognition has also come to the fore. In particular, many studies have focused on object recognition based on markerless matching. However, most of these studies have focused on desktop systems, which can have high performance in terms of CPU and memory, rather than investigating the use of mobile systems, which have been previously unable to provide high-performance object recognition based on markerless matching. In this paper, we propose a method that uses the OpenCV mobile library to improve real-time object recognition performance on mobile systems. First, we investigate the original object recognition algorithm to identify performance bottlenecks. Second, we optimize the algorithm by analyzing each module and applying appropriate code enhancements. Last, we change the operational structure of the algorithm to improve its performance, changing the execution frequency of the object recognition task from every frame to every four frames for real-time operation. During the three frames in which the original method is not executed, the object is instead recognized using the mobile devices accelerometer. We carry out experiments to reveal how much each aspect of our method improves the overall object recognition performance; overall, experimental performance improves by approximately 800 %, with a corresponding reduction of approximately 1 % in object recognition accuracy. Therefore, the proposed technique can be used to significantly improve the performance of object recognition based on markerless matching on mobile systems for real-time operation.     

Making Augmented Reality Practical on Mobile Phones, Part 2

Part 1 of this article introduced a software environment for augmented reality (AR) on mobile phones, discussed development and debugging strategies, and showed how to execute several tasks of a common AR system in parallel on a mobile device. Part 2 discusses how to overcome the most severe limitations such as memory, rendering speed, and computational power. The authors analyze where an optimized mobile phone AR application spends most of its processing time and give an outlook on what to expect in the next few years.     

Buddy算法的μC/OS-Ⅱ高可靠内存管理方案

针对现有μC/OS-Ⅱ内存管理方案分配内存不灵活、可靠性不高的特点,提出一种适用于μC/OS-Ⅱ增强内存管理可靠性的方案.该方案借鉴Buddy算法的思想,将可用内存划分为一系列2的幂次方规模大小的内存块,申请小块内存得不到分配时可以将大块内存块平分后得到满足.回收内存块时,地址连续的相同大小的内存块可以合并成大内存块,...