遥感器像移与图像参数的分析建模研究

卫星平台运动会产生恶化像质的像移,像移是影响遥感图像质量的重要因素.针对这种情况,建立了像移和图像参数的关系模型,可通过卫星生成图像的内在特征定量研究卫星成像过程中产生的像移,而不必通过产生像移的卫星平台等因素建立物理模型来研究像移.首先模拟了遥感器在轨成像过程,通过人工制造出影响因素的干扰获得了仿真图像数据,然后定量分析了像移与图像参数之间的相关性,选出了相关性较强且适合建模的图像参数,最后通过对这些图像参数构成的向量的降维得到了反映图像质量的综合参数,并建立了综合参数与像移的关系模型,以通过图像综合参数来计算像移.实验结果表明,这种模型能够很好地反映图像综合指标与像移之间的关系.该研究通过图像参数定量研究像移,为在轨运行卫星的遥感器像移与外景条件下的遥感图像要素的相关建模研究提供了部分技术支持.     

基于主题隐马尔科夫模型的人体异常行为识别

针对基于监控视频的人体异常行为识别问题,提出了基于主题隐马尔科夫模型的人体异常行为识别方法,即通过无任何人工标注的视频训练集自动学习人体行为模型,并能够应用学到的人体行为模型实时检测异常行为和识别正常行为。这一方法主要围绕"低层视频表示-中层语义行为建模-高层语义分类"3个方面进行:1)基于时-空间兴趣点构建了一种紧凑的和有效的视频表示方法。2)提出一种新颖的语义主题模型(Topic Model,TM)——主题隐马尔科夫模型(Topic Hidden Markov Model,THMM),它能够自然地分组视频中检测到的人体行为。主题隐马尔科夫模型基于已有的马尔科夫模型和主题模型构造,不但聚类运动词汇成简单动作,而且聚类简单动作成全局行为,同时建模了行为时间上的相关性。THMM是一个4层贝叶斯主题模型,它将视频序列建模为行为的马尔科夫链,同时行为是视频序列中某些视频剪辑(Clip)的概率分布;将视频剪辑建模为动作的随机组合,同时动作是视频剪辑中运动词汇的概率分布。克服了传统隐马尔科夫模型和主题模型在人体复杂行为建模过程中精度、鲁棒性和计算效率上的不足。3)提出运行时累积的异常性测度及其在线异常行为检测方法和基于在线似然比检验(Likelihood Ratio Test,LRT)的实时正常行为分类方法,从而克服了实时行为识别过程中由于缺乏充分的视觉证据而引发的行为类型歧义,能完较好地完成监控场景中实时异常行为检测和在线正常行为识别的任务。取自实际监控场景的实验数据集上的实验结果证明了本方法的有效性。     

分层运动估值器的VLSI结构实现

提出了一种分层算法的VLSI结构设计方法，该结构使用同一个基本搜索单元来完成整个三层搜索过程，减少了芯片的尺寸和功耗，同时，支持低比特视频编码器（如H．263和MPEG-4）的高级预测模式，在不增加额外计算负担的情况下，通过对数据流的有效控制，在获得宏块运动矢量的同时，可以获得该宏块4个8x8子块的运动矢量。     

Reusing Primitive and Acquired Motion Knowledge for Gait Generation of a Six-legged Robot Using Genetic Programming

There has been growing interest in motion planning problems for mobile robots. In this field, the main research is to generate a motion for a specific robot and task without previously acquired motions. However it is too wasteful not to use hard-earned acquired motions for other tasks. Here, we focus on a mechanism of reusing acquired motion knowledge and study a motion planning system able to generate and reuse motion knowledge. In this paper, we adopt a tree-based representation for expressing knowledge of motion, and propose a hierarchical knowledge for realizing a reuse mechanism. We construct a motion planning system using hierarchical knowledge as motion knowledge and using genetic programming as a learning method. We apply a proposed method for the gait generation task of a six-legged locomotion robot and show its availability with computer simulation.     

Vanishing points and three-dimensional lines from omni-directional video

This paper describes a system for structure from motion using vanishing points and three-dimensional lines extracted from omni-directional video sequences. To track lines, we use a novel dynamic programming approach to improve ambiguity resolution, and we use delayed states to aid in the initialization of landmarks. By reobserving vanishing points we get direct measurements of the robots three-dimensional attitude that are independent of its position. Using vanishing points simplifies the representation since parallel lines share the same direction states. We show the performance of the system in various indoor and outdoor environments and include comparisons with independent two-dimensional reference maps for each experiment .     

Calibrated,Registered Images of an Extended Urban Area

We describe a dataset of several thousand calibrated, time-stamped, geo-referenced, high dynamic range color images, acquired under uncontrolled, variable illumination conditions in an outdoor region spanning several hundred meters. The image data is grouped into several regions which have little mutual inter-visibility. For each group, the calibration data is globally consistent on average to roughly five centimeters and 0 1°, or about four pixels of epipolar registration. All image, feature and calibration data is available for interactive inspection and downloading at http://city.lcs.mit.edu/data.Calibrated imagery is of fundamental interest in a variety of applications. We have made this data available in the belief that researchers in computer graphics, computer vision, photogrammetry and digital cartography will find it of value as a test set for their own image registration algorithms, as a calibrated image set for applications such as image-based rendering, metric 3D reconstruction, and appearance recovery, and as input for existing GIS applications.     

基于一维标定物的多摄像机标定

一维标定物是由三个或三个以上彼此距离已知的共线点构成的. 现有文献指出只有当一维标定物做平面运动或者绕固定点转动时,才能实现摄像机的标定. 本文的研究结果表明,当多个摄像机同时观察作任意刚体运动的一维标定物时,则该摄像机组能被线性地标定. 本文给出一种线性标定算法,并使用最大似然准则对线性算法结果进行精化. 模拟实验和真实图像实验都表明本文的算法是有效可行的.     

Parallel Planning: A New Motion Planning Framework for Autonomous Driving

Motion planning is one of the most significant technologies for autonomous driving. To make motion planning models able to learn from the environment and to deal with emergency situations, a new motion planning framework called as "parallel planning" is proposed in this paper. In order to generate sufficient and various training samples, artificial traffic scenes are firstly constructed based on the knowledge from the reality. A deep planning model which combines a convolutional neural network (CNN) with the Long Short-Term Memory module (LSTM) is developed to make planning decisions in an end-toend mode. This model can learn from both real and artificial traffic scenes and imitate the driving style of human drivers. Moreover, a parallel deep reinforcement learning approach is also presented to improve the robustness of planning model and reduce the error rate. To handle emergency situations, a hybrid generative model including a variational auto-encoder (VAE) and a generative adversarial network (GAN) is utilized to learn from virtual emergencies generated in artificial traffic scenes. While an autonomous vehicle is moving, the hybrid generative model generates multiple video clips in parallel, which correspond to different potential emergency scenarios. Simultaneously, the deep planning model makes planning decisions for both virtual and current real scenes. The final planning decision is determined by analysis of real observations. Leveraging the parallel planning approach, the planner is able to make rational decisions without heavy calculation burden when an emergency occurs.      

基于双层微结构的碳纳米管柔性应变传感器的研究

设计了一种具有双层微结构的柔性压阻式应变传感器,并进行了制备方法研究。通过在半固化的环氧树脂基底(Ecoflex~? 00-30)表面制备形成碳纳米管(MCNTs)/环氧树脂(Ecoflex~? 00-30)复合薄膜,用毛刷制备微结构。通过测试分析可得,该柔性应变传感器具有高度可拉伸性能,可承受200%的应变,在0～80%,80%～170%,170%～195%的应变区间,GF值分别为10.5,40.5,190.5。将传感器进行可穿戴运动监测测试,可验证本传感器既可以实现手指和手腕运动等大动作应变的监测,输出电阻值相对变化达到7.5倍;也可以监测肌肉形变弯曲等小应变,输出电阻值相对变化达到1.4倍。     

Differences between oculomotor and perceptual artifacts for temporally limited head mounted displays

We used perceptual and oculomotor measures to understand the negative impacts of low (phantom array) and high (motion blur) duty cycles with a high‐speed, AR‐likehead‐mounted display prototype. We observed large intersubject variability for the detection of phantom array artifacts but a highly consistent and systematic effect on saccadic eye movement targeting during low duty cycle presentations. This adverse effect on saccade endpoints was also related to an increased error rate in a perceptual discrimination task, showing a direct effect of display duty cycle on the perceptual quality. For high duty cycles, the probability of detecting motion blur increased during head movements, and this effect was elevated at lower refresh rates. We did not find an impact of the temporal display characteristics on compensatory eye movements during head motion (e.g., VOR). Together, our results allow us to quantify the tradeoff of different negative spatiotemporal impacts of user movements and make subsequent recommendations for optimized temporal HMD parameters.