一种基于视觉知识加工模型的目标识别方法

提出了一种基于视觉知识加工模型的目标识别方法. 该加工模型结合目标定位、模板筛选和MFF-HMAX (Hierarchical model and X based on multi-feature fusion)方法对图像进行学习, 形成相应的视觉知识库, 并用于指导目标的识别. 首先, 利用Itti模型获取图像的显著区, 结合视觉通路中What和Where通道的位置、大小等特征以及视觉知识库中的定位知识确定初期候选目标区域; 然后, 采用二步去噪处理获取候选目标区域, 利用MFF-HMAX模型提取目标区域的颜色、亮度、纹理、轮廓、大小等知识特征, 并采用特征融合思想将各项特征融合供目标识别; 最后, 与单一特征以及目前的流行方法进行对比实验, 结果表明本文方法不仅具备较高的识别效果, 同时能够模仿人脑学习视觉知识的过程形成视觉知识库.     

Development of a physically behaved robot work cell in virtual reality for task teaching

Robot task teaching on a real work cell is expensive and sometimes risky. This cost and risk can be avoided by using virtual reality technology. Using the simulated environment in virtual reality (VR), the operator can practise, explore and preview the operations for possible problems that might occur during implementation. It is therefore of practical importance to build the virtual robot work cell in VR that can facilitate the study of the performance of robotic tasks such as robotic assembly. This paper describes our work in incorporating physical behaviours of virtual objects into VR for robot task teaching. To facilitate the task teaching, we developed visual and audio cues which help visualise the dynamic interactions between virtual objects. Dynamic sensing capability is incorporated in the simulated environment. A simplified force sensor is modelled and simulated. The physical behaviours of the virtual objects are simulated using physics-based approach. A virtual robot work cell is built incorporating the developed features and an example for the task teaching is given. The implementation includes view tracking using virtual camera, visual and audio rendering, and the user interface developed in the VR. The current implementation was carried out on a PC-based VR platform, with the programs developed using Watcom C++.     

Intelligent and Efficient Strategy for Unstructured Environment Sensing Using Mobile Robot Agents

In field environments it is not usually possible to provide robots in advance with valid geometric models of its task and environment. The robot or robot teams need to create these models by scanning the environment with its sensors. Here, an information-based iterative algorithm to plan the robot's visual exploration strategy is proposed to enable it to most efficiently build 3D models of its environment and task. The method assumes mobile robot (or vehicle) with vision sensors mounted at a manipulator end-effector (eye-in-hand system). This algorithm efficiently repositions the systems' sensing agents using an information theoretic approach and fuses sensory information using physical models to yield a geometrically consistent environment map. This is achieved by utilizing a metric derived from Shannon's information theory to determine optimal sensing poses for the agent(s) mapping a highly unstructured environment. This map is then distributed among the agents using an information-based relevant data reduction scheme. This method is particularly well suited to unstructured environments, where sensor uncertainty is significant. Issues addressed include model-based multiple sensor data fusion, and uncertainty and vehicle suspension motion compensation. Simulation results show the effectiveness of this algorithm.     

Which animation effects improve indirect manipulation?

This paper describes an experiment to explore the effectiveness of animation in improving indirect manipulation operations. Indirect manipulation operations are those initiated by command menus and buttons to perform a transformation on a graphical object or set of graphical objects. The particular improvement is an operation's ability to show both what would happen if the operation were committed and what would happen if it were cancelled while an operation is being considered. The experiment required subjects to watch a simple alignment operation for a set of graphical objects. They were then asked to record the original placement of those graphical objects. Each task used one of four visual cues: modified telltale, wiggle, colour, or no visual cue. We found the modified telltale, wiggle, and colour visual effects significantly more effective than no visual feedback for cuing the user as to original position of the graphical objects. The modified telltale and colour effects were significantly more effective than the wiggle effect. The major conclusion drawn from this experiment is the use of visual feedback improves a user's ability to remember the previous position of graphical objects after an alignment operation.     

Sensor explication: knowledge-based robotic plan execution throughlogical objects

Complex robot tasks are usually described as high level goals, with no details on how to achieve them. However, details must be provided to generate primitive commands to control a real robot. A sensor explication concept that makes details explicit from general commands is presented. We show how the transformation from high-level goals to primitive commands can be performed at execution time and we propose an architecture based on reconfigurable objects that contain domain knowledge and knowledge about the sensors and actuators available. Our approach is based on two premises: 1) plan execution is an information gathering process where determining what information is relevant is a great part of the process; and 2) plan execution requires that many details are made explicit. We show how our approach is used in solving the task of moving a robot to and through an unknown, and possibly narrow, doorway; where sonic range data is used to find the doorway, walls, and obstacles. We illustrate the difficulty of such a task using data from a large number of experiments we conducted with a real mobile robot. The laboratory results illustrate how the proper application of knowledge in the integration and utilization of sensors and actuators increases the robustness of plan execution.     

非结构环境下基于人机合作技术的抓取作业研究

介绍了一种利用人机合作技术在非结构环境引导机械手抓取静态目标的方法．分别介绍了将激光—CCD摄像机系统与操作者的经验相结合获得抓取目标位置的方法,及将虚拟现实技术与操作者的经验相结合获得抓取目标姿态的方法．继而利用基于模型的视觉引导技术,引导手臂完成抓取操作．     

基于遥感影像多尺度分析技术的LUCC研究

基于遥感和地理信息系统技术研究土地利用／覆被变化（LUCC）目前普遍采用的方法，其中研究区不同时期遥感影像土地利用／覆被信息的获取是研究的关键，而传统的单一尺度的信息自动提取方式很难满足高精度LUCC研究的需要。为此，提出了以多尺度分析技术来提取土地利用／覆被信息的方法，并以江津市的LUCC研究为例，显示出这种方法的优越性。     

注意力分配策略对界面任务绩效和视觉行为的影响机制

在工程领域,作业人员通常需要面对刺激分布不均的复杂信息界面,并执行相关的交互任务.作业人员的视觉注意力分配已被证明与任务绩效密切相关,但对于复杂界面中基于不同信息分配策略的多优先级刺激对作业人员的视觉注意力分配及任务绩效间的潜在联系仍亟待研究.对此,本文基于多优先级注意力分配策略实验对作业人员在不同负荷条件下的任务绩效和视觉行为的影响机制展开研究.实验结果表明,差异性的分配策略和信息优先级划分提升了任务绩效表现,不同分配策略和优先级划分条件下的视觉行为存在显著差异,并受脑力负荷的影响.该结论能够为人机交互界面的设计和优化提供参考,从而提高作业人员在任务中的绩效表现.     

Signal transmission model for the substations grounding grid

The signal of the wireless sensor network in grounding grid, owing to energy loss, network congestion, path constraints and other factors, is easy to delay even partially losing. In order to ensure that the signal can be transmitted effectively in grounding grids for the substation, this paper presents a method based on traffic model of back-off balanced multiple sensor network cooperation model. As we all know, cognitive radio (CR) technology is adopted in multi-channel wireless networks to provide enough channels for data transmission. The MAC protocols should enable the secondary users to maintain the accurate channel state information to identify and utilize the leftover frequency spectrum in a way that constrains the level of interference to the primary users. We proposed a novel cooperation spectrum sensing scheme in which the secondary users adopt backoff-based sensing policy based on the traffic model of the primary users to maximum the throughput of the network. To obtain the full accurate information of the spectrum is a difficult task so that we propose the backoff sensing as a sub-optimal strategy. Since the secondary users sense only a subset of the channels in our proposed scheme, less time is spent to get the channel state information as more time is saved for the data transmission. And while dealing the signal data, I combine the intensity transfer method instead of the priority method. This can effectively reduce the network congestion, to ensure that the main information can be transfer well. It is also very useful to signal transmission for the Multi-sensor in Substations Grounding Grid (SGG).     

Active Visual Perception for Mobile Robot Localization

Localization is a key issue for a mobile robot, in particular in environments where a globally accurate positioning system, such as GPS, is not available. In these environments, accurate and efficient robot localization is not a trivial task, as an increase in accuracy usually leads to an impoverishment in efficiency and viceversa. Active perception appears as an appealing way to improve the localization process by increasing the richness of the information acquired from the environment. In this paper, we present an active perception strategy for a mobile robot provided with a visual sensor mounted on a pan-tilt mechanism. The visual sensor has a limited field of view, so the goal of the active perception strategy is to use the pan-tilt unit to direct the sensor to informative parts of the environment. To achieve this goal, we use a topological map of the environment and a Bayesian non-parametric estimation of robot position based on a particle filter. We slightly modify the regular implementation of this filter by including an additional step that selects the best perceptual action using Monte Carlo estimations. We understand the best perceptual action as the one that produces the greatest reduction in uncertainty about the robot position. We also consider in our optimization function a cost term that favors efficient perceptual actions. Previous works have proposed active perception strategies for robot localization, but mainly in the context of range sensors, grid representations of the environment, and parametric techniques, such as the extended Kalman filter. Accordingly, the main contributions of this work are: i) Development of a sound strategy for active selection of perceptual actions in the context of a visual sensor and a topological map; ii) Real time operation using a modified version of the particle filter and Monte Carlo based estimations; iii) Implementation and testing of these ideas using simulations and a real case scenario. Our results indicate that, in terms of accuracy of robot localization, the proposed approach decreases mean average error and standard deviation with respect to a passive perception scheme. Furthermore, in terms of efficiency, the active scheme is able to operate in real time without adding a relevant overhead to the regular robot operation.     

An agent-based approach to multisensor coordination

This paper presents an automated system for multiple sensor placement based on the coordinated decisions of independent, intelligent agents. The problem domain is such that a single sensor system would not provide adequate information for a given sensor task. Hence, it is necessary to incorporate multiple sensors in order to obtain complete information. The overall goal of the system is to provide the surface coverage necessary to perform feature inspection on one or more target objects in a cluttered scene. This is accomplished by a group of cooperating intelligent sensors. In this system, the sensors are mobile, the target objects are stationary and each agent controls the position of a sensor and has the ability to communicate with other agents in the environment. By communicating desires and intentions, each agent develops a mental model of the other agents' preferences, which is used to avoid or resolve conflict situations. In this paper we utilize cameras as the sensors. The experimental results illustrate the feasibility of the autonomous deployment of the sensors and that this deployment can occur with sufficient accuracy as to allow the inspection task to be performed.     

视频传感器网络中基于相关性图像融合算法

基于图像相关性信息,从系统层面提出了一种图像融合算法.该算法充分考虑到视频传感器节点的有限资源及相邻节点间冗余视觉信息,将同一场景的视觉监测任务分配到相关度较大的2个视频传感器节点上,每个视频传感器节点仅负责传输一部分视觉场景信息.特别地,利用极线约束性质融合多路传输来的部分图像信息,最终实现场景视觉信息的重建.实验结果表明:该算法简单易行,既可以减少网络传输量、节约网络能量,又可以实现场景视觉信息的有效监测.     

Information-Driven Sensor Path Planning by Approximate Cell Decomposition

A methodology is developed for planning the sensing strategy of a robotic sensor deployed for the purpose of classifying multiple fixed targets located in an obstacle-populated workspace. Existing path planning techniques are not directly applicable to robots whose primary objective is to gather sensor measurements using a bounded field of view (FOV). This paper develops a novel approximate cell-decomposition method in which obstacles, targets, sensor's platform, and FOV are represented as closed and bounded subsets of an Euclidean workspace. The method constructs a connectivity graph with observation cells that is pruned and transformed into a decision tree from which an optimal sensing strategy can be computed. The effectiveness of the optimal sensing strategies obtained by this methodology is demonstrated through a mine-hunting application. Numerical experiments show that these strategies outperform shortest path, complete coverage, random, and grid search strategies, and are applicable to nonoverpass capable robots that must avoid targets as well as obstacles.     

基于无线信号的人类行为检测和识别

物联网技术实现了物与物、人与物的全面互联,其中信息传感设备与人的交互需要对人体行为活动进行感知。目前广泛使用的有基于视觉和利用穿戴式传感器的识别方法,但这些方法在很多场景下应用有所限制。文章提出一种基于无线信号识别人类行为的方法,通过对通信中传输数据包状态的统计和分析,能够利用少量通信节点达到感知非携带设备的目标在室内检测区域行为活动的目的。对于不同的行为活动特征,采用序列最小优化算法、 K-最近邻算法等不同算法进行分类研究。相对于传统基于无线信号接收信号强度指标的免携带设备行为识别方法,文章提出的方法对不同运动速度等级的识别精度平均提高了 25.1%。     

纹理合成技术在遥感影像缺失信息恢复中的应用

纹理合成技术是目前计算机图形图像领域研究的一个热点,特别是基于样本的纹理合成,可以根据用户给定的纹理样本,生成具有相同视觉效果的大面积纹理图像,在图像缺失信息修复、目标物去除等方面具有很大的应用价值。本文采用基于马尔可夫随机场原理的样本纹理合成方法,对遥感影像上可能出现的遮挡区域进行恢复弥补,并针对遥感影像的特点,引入相应的GIS辅助数据,提高算法处理的可靠性和视觉质量。通过实验表明,该方法取得了较好的恢复效果,在遥感应用中具有一定的实用价值。     

A remote sensing ship recognition method based on dynamic probability generative model

Aiming at detecting sea targets reliably and timely, a novel ship recognition method using optical remote sensing data based on dynamic probability generative model is presented. First, with the visual saliency detection method, prior shape information of target objects in put images which is used to describe the initial curve adaptively is extracted, and an improved Chan–Vese (CV) model based on entropy and local neighborhood information is utilized for image segmentation. Second, based on rough set theory, the common discernibility degree is used to compute the significance weight of each candidate feature and select valid recognition features automatically. Finally, for each node, its neighbor nodes are sorted by their ε-neighborhood distances to the node. Using the classes of the selected nodes from top of sorted neighbor nodes list, a dynamic probability generative model is built to recognize ships in data from optical remote sensing system. Experimental results on real data show that the proposed approach can get better classification rates at a higher speed than the k-nearest neighbor (KNN), support vector machines (SVM) and traditional hierarchical discriminant regression (HDR) method.     

基于新型多模态触觉传感器的机器人交互物体分类

多模态传感融合对于机器人探索外界环境十分重要,而现有的触觉传感器只能收集一种触觉模态信息,其收集到的多模态信息存在弱配对问题,为了解决此问题,研究了一种基于触觉和视觉融合的多模态触觉传感器。该传感器可以利用一个感知层同时收集2种异构触觉模态信息,弥补了传统触觉传感器的缺陷,同时可以利用收集到的多模态信息对不同物体的几何形状进行分类,在研究过程中,通过按压实验收集了圆形、正方形、长方形和三角形4种形状物体的触觉信息,再利用K最近邻(KNN)算法进行几何形状分类,实验结果证明了该传感器在区分不同物体的形状上具有良好的效果。     

A temporal-spatial background modeling of dynamic scenes

Moving object detection in dynamic scenes is a basic task in a surveillance system for sensor data collection. In this paper, we present a powerful background subtraction algorithm called Gaussian-kernel density estimator (G-KDE) that improves the accuracy and reduces the computational load. The main innovation is that we divide the changes of background into continuous and stable changes to deal with dynamic scenes and moving objects that first merge into the background, and separately model background using both KDE model and Gaussian models. To get a temporal-spatial background model, the sample selection is based on the concept of region average at the update stage. In the detection stage, neighborhood information content (NIC) is implemented which suppresses the false detection due to small and un-modeled movements in the scene. The experimental results which are generated on three separate sequences indicate that this method is well suited for precise detection of moving objects in complex scenes and it can be efficiently used in various detection systems.