The understanding and analysis of video content are fundamentally important for numerous applications,including video summarization,retrieval,navigation,and editing.An important part of this process is to detect salient (which usually means important and interesting) objects in video segments.Unlike existing approaches,we propose a method that combines the saliency measurement with spatial and temporal coherence.The integration of spatial and temporal coherence is inspired by the focused attention in human vision.In the proposed method,the spatial coherence of low-level visual grouping cues (e.g.appearance and motion) helps per-frame object-background separation,while the temporal coherence of the object properties (e.g.shape and appearance) ensures consistent object localization over time,and thus the method is robust to unexpected environment changes and camera vibrations.Having developed an efficient optimization strategy based on coarse-to-fine multi-scale dynamic programming,we evaluate our method using a challenging dataset that is freely available together with this paper.We show the effectiveness and complementariness of the two types of coherence,and demonstrate that they can significantly improve the performance of salient object detection in videos.
WU YangZHENG NanNingYUAN ZeJianJIANG HuaiZuLIU Tie
针对移动机器人同时定位与地图创建(Simultaneous localization and mapping,SLAM)中的FastSLAM算法,存在非线性系统线性化处理和计算雅可比矩阵的缺点,本文提出了基于Sterling多项式插值处理非线性系统的SLAM方法.该方法基于Rao-Blackwellized粒了滤波框架,利用中心差分滤波方法产生改进的建议分布函数,提高了机器人位姿估计的精度;利用中心差分滤波初始化特征和更新地图中的特征,提高了地图创建的精度;针对实际应用中存在虚假特征的情况提出了一种有效的地图管理方法.在同等粒了数的情况下,该方法改进了SLAM结果的精度.基于仿真和实际数据的实验结果验证了该方法的有效性.
