异类传感器实时信息融合的STMHM算法

异类传感器实时信息融合由于两类设备的数据率不同和数据误差的限制,一直未得到有效解决,提出一种空时二维多假设模型(STMHM)算法来解决该问题。设计的融合数据模型,将异类传感器的测量数据映射在二维直角坐标系下;按照新的融合数据模型分别构建两类传感器的目标量测空间和融合空间;设计量测空间时间初始化方法和目标实体空间的滤波算法;通过仿真验证表明:该算法能够实现异类传感器的信息融合,初次融合成功的确认时间在3～5个主动传感器扫描周期。     

基于颜色空间映射的自适应足球机器人视觉系统

为了解决足球机器人比赛中视觉信息检测问题,给出基于嵌入式多处理器体系结构的视觉系统框架,并研究了一种自适应阈值目标分割方法。该方法将视觉系统采集的图像从YCbCr颜色空间转换为一维颜色映射图像,并提出基于竞争学习的空间映射方法。同时使用小波变换方法来平滑颜色映射空间直方图和降低阈值搜索维数。然后使用基于映射图像直方图动态搜索阈值的方法来分割图像。最后,通过实验表明研究的视觉系统的有效性,能够实时准确地完成视觉处理任务。     

利用改进的差分进化算法求解等圆Packing问题

等圆Packing问题属于强约束的复杂组合优化问题之一,针对其强约束特点及难点,通过改进传统的差分进化算法,提出一种等圆Packing问题的求解方法。该改进算法特点是将有效解空间加入差分进化的变异约束中,并采用随机排序机制改进差分进化的选择机制。通过多次实验,表明此算法在求解小规模等圆Packing问题上取得的效果与目前所能找到的最优值相差不到0.6%,从而验证了演化计算在求解等圆Packing问题的可行性;与此同时,演化算法具有很好的收敛性,因此在其他强约束的复杂优化问题上将有很好的应用。     

基于SIFT特征的遥感图像配准方法研究与实现

讨论了基于SIFT特征的遥感图像配准方法,重点介绍了基于特征点提取的算法描述以及针对基于特征点的遥感图像配准算法流程,并通过实验进行了验证。     

对偶四元数线阵遥感影像几何定位

提出以对偶四元数为数学工具进行线阵CCD(电荷耦合元件)遥感影像几何定位的全新技术方法。利用对偶四元数建立遥感通用传感器严密成像模型,将光线束的位置和姿态统一用对偶四元数表示,通过传感器扫描光线在空间中的螺旋运动,实现像点到其对应地面点物方坐标的变换,从而克服了成像几何参数(外方位元素)之间的强相关性。按照空间刚体变换线性蒙皮混合理论,可以把刚体变换矩阵分解为平移和旋转两个部分,对平移部分进行线性插值,对旋转部分进行球面插值,从而实现线阵CCD遥感影像外方位元素的解算。按照所建立的成像几何模型,利用某地区Geoeye-1遥感影像进行几何定位实验,实验结果表明新算法获得的几何定位精度优于传统算法,能够解决定位参数之间的相关性问题。     

Ontological reasoning in the design space exploration of advanced cyber–physical systems

Cyber–physical systems are becoming increasingly complex. In these advanced systems, the different engineering domains involved in the design process become more and more intertwined. Therefore, a traditional (sequential) design process becomes inefficient in finding good design options. Instead, an integrated approach is needed where parameters in multiple different engineering domains can be chosen, evaluated, and optimized to achieve a good overall solution. However, in such an approach, the combined design space becomes vast. As such, methods are needed to mitigate this problem.In this paper, we show a method for systematically capturing and updating domain knowledge in the context of a co-design process involving different engineering domains, i.e. control and embedded. We rely on ontologies to reason about the relationships between parameters in the different domains. This allows us to derive a stepwise design space exploration workflow where this domain knowledge is used to quickly reduce the design space to a subset of likely good candidates. We illustrate our approach by applying it to the design space exploration process for an advanced electric motor control system and its deployment on embedded hardware.     

Searching minimax game trees under memory space constraint

Games such as CHESS, GO and OTHELLO can be represented by minimax game trees. Among various search procedures to solve such game trees,- and SSS^* are perhaps most well known. Although it is proved that SSS^* explores only a subset of the nodes explored by-, - is commonly believed to be faster in real applications, since it requires very little memory space and hence its storage management cost is low. Contrary to this folklore, however, this paper reports, using the OTHELLO game as an example, that SSS^* is much faster than-. It is also demonstrated that SSS^* can be modified to make the required memory space controllable to some extent, while retaining the high efficiency of the original SSS^*.This research was partially supported by the Ministry of Education, Science and Culture of Japan, under a Scientific Grant-in-Aid.     

No-reference stereoscopic image quality assessment using quaternion wavelet transform and heterogeneous ensemble learning

As the demand for high-quality stereo images has grown in recent years, stereoscopic image quality assessment (SIQA) has become an important research area in modern image processing technology.In this paper, we propose a no-reference stereoscopic image quality assessment (NR-SIQA) model using heterogeneous ensemble learning ‘quality-aware’ features from luminance image, chrominance image, disparity and cyclopean images via quaternion wavelet transform (QWT). Firstly, luminance image and chrominance image are generated by CIELAB color space as monocular perception, and the novel disparity and cyclopean images are utilized to complement with monocular information. Then, a number of ‘quality-aware’ features in the quaternion wavelet domain are discovered, including entropy, texture features, energy features, energy differences features and MSCN coefficients of high frequency sub-band. Finally, a heterogeneous ensemble model via support vector regression (SVR) & extreme learning machine (ELM) & random forest (RF) is proposed to predict quality score, and bootstrap sampling and rotated feature space are used to increase the diversity of data distribution. Comparing with the state-of-the-art NR-SIQA models, experimental results on four public databases prove the accuracy and robustness of the proposed model.     

基于目标导向行为和空间拓扑记忆的视觉导航方法

针对在具有动态因素且视觉丰富环境中的导航问题,受路标机制空间记忆方式启发,提出一种可同步学习目标导向行为和记忆空间结构的视觉导航方法.首先,为直接从原始输入中学习控制策略,以深度强化学习为基本导航框架,同时添加碰撞预测作为模型辅助任务;然后,在智能体学习导航过程中,利用时间相关性网络祛除冗余观测及寻找导航节点,实现通过情景记忆递增描述环境结构;最后,将空间拓扑地图作为路径规划模块集成到模型中,并结合动作网络用于获取更加通用的导航方法.实验在3D仿真环境DMlab中进行,实验结果表明,本文方法可从视觉输入中学习目标导向行为,在所有测试环境中均展现出更高效的学习方法和导航策略,同时减少构建地图所需数据量;而在包含动态堵塞的环境中,该模型可使用拓扑地图动态规划路径,从而引导绕路行为完成导航任务,展现出良好的环境适应性.