Copy-Move Forgery(CMF) is one of the simple and effective operations to create forged digital images.Recently,techniques based on Scale Invariant Features Transform(SIFT) are widely used to detect CMF.Various approach...Copy-Move Forgery(CMF) is one of the simple and effective operations to create forged digital images.Recently,techniques based on Scale Invariant Features Transform(SIFT) are widely used to detect CMF.Various approaches under the SIFT-based framework are the most acceptable ways to CMF detection due to their robust performance.However,for some CMF images,these approaches cannot produce satisfactory detection results.For instance,the number of the matched keypoints may be too less to prove an image to be a CMF image or to generate an accurate result.Sometimes these approaches may even produce error results.According to our observations,one of the reasons is that detection results produced by the SIFT-based framework depend highly on parameters whose values are often determined with experiences.These values are only applicable to a few images,which limits their application.To solve the problem,a novel approach named as CMF Detection with Particle Swarm Optimization(CMFDPSO) is proposed in this paper.CMFD-PSO integrates the Particle Swarm Optimization(PSO) algorithm into the SIFT-based framework.It utilizes the PSO algorithm to generate customized parameter values for images,which are used for CMF detection under the SIFT-based framework.Experimental results show that CMFD-PSO has good performance.展开更多
为了解决冗余机械臂在复杂环境中的路径规划和避障问题,提出一种基于改进快速扩展随机树(Rapidly Exploring Random Tree,RRT)算法与三维碰撞检测的高效路径规划方法。利用改进算法生成无碰撞的平滑路径,对机器人姿态进行求解,并通过碰...为了解决冗余机械臂在复杂环境中的路径规划和避障问题,提出一种基于改进快速扩展随机树(Rapidly Exploring Random Tree,RRT)算法与三维碰撞检测的高效路径规划方法。利用改进算法生成无碰撞的平滑路径,对机器人姿态进行求解,并通过碰撞检测验证路径的可行性。改进的RRT算法采用基于概率的控制机制来优化随机点生成策略,结合路径平滑算法减少路径节点,同时引入三维碰撞检测技术以确保路径的有效性和安全性。试验结果表明:该方法在二维和三维复杂场景中均能显著提升路径规划效率,成功率和路径平滑性明显优于传统算法。研究成果可为冗余机械臂在复杂环境中的路径规划提供高效、可靠的解决方案,有助于进一步提升其在实际应用中的稳定性和适用性。展开更多
针对在机器人辅助头颈部手术中双机械臂进行牵拉易产生碰撞且对目标软组织造成损伤问题,文中基于传统Informed-RRT*(Informed-Rapidly Exploring Random Tree*)路径规划算法叠加引力场降低路径搜索的盲目性。为优化传统Informed-RRT*路...针对在机器人辅助头颈部手术中双机械臂进行牵拉易产生碰撞且对目标软组织造成损伤问题,文中基于传统Informed-RRT*(Informed-Rapidly Exploring Random Tree*)路径规划算法叠加引力场降低路径搜索的盲目性。为优化传统Informed-RRT*路径规划算法存在导向性差和效率低等缺点,引入回归滤波机制避免搜索陷入局部最优,对步长进行动态调节。同时,优化了规划路径,采用冗余节点剔除策略,去除了冗余节点,提高了路径的平滑性。基于双机械臂碰撞检测方法和改进Informed-RRT*算法对双臂协调路径规划方法进行了研究。通过仿真实验可知,与原有算法相比,所提算法的迭代时间降低了72.76%,迭代次数降低了46.39%,平均路径长度约缩短6%,节点数约减少45%,验证了改进规划算法的有效性。展开更多
基金supported in part by the National Natural Science Foundation of China under grant No.(61472429,61070192,91018008,61303074,61170240)Beijing Natural Science Foundation under grant No.4122041+1 种基金National High-Tech Research Development Program of China under grant No.2007AA01Z414National Science and Technology Major Project of China under grant No.2012ZX01039-004
文摘Copy-Move Forgery(CMF) is one of the simple and effective operations to create forged digital images.Recently,techniques based on Scale Invariant Features Transform(SIFT) are widely used to detect CMF.Various approaches under the SIFT-based framework are the most acceptable ways to CMF detection due to their robust performance.However,for some CMF images,these approaches cannot produce satisfactory detection results.For instance,the number of the matched keypoints may be too less to prove an image to be a CMF image or to generate an accurate result.Sometimes these approaches may even produce error results.According to our observations,one of the reasons is that detection results produced by the SIFT-based framework depend highly on parameters whose values are often determined with experiences.These values are only applicable to a few images,which limits their application.To solve the problem,a novel approach named as CMF Detection with Particle Swarm Optimization(CMFDPSO) is proposed in this paper.CMFD-PSO integrates the Particle Swarm Optimization(PSO) algorithm into the SIFT-based framework.It utilizes the PSO algorithm to generate customized parameter values for images,which are used for CMF detection under the SIFT-based framework.Experimental results show that CMFD-PSO has good performance.
文摘为了解决冗余机械臂在复杂环境中的路径规划和避障问题,提出一种基于改进快速扩展随机树(Rapidly Exploring Random Tree,RRT)算法与三维碰撞检测的高效路径规划方法。利用改进算法生成无碰撞的平滑路径,对机器人姿态进行求解,并通过碰撞检测验证路径的可行性。改进的RRT算法采用基于概率的控制机制来优化随机点生成策略,结合路径平滑算法减少路径节点,同时引入三维碰撞检测技术以确保路径的有效性和安全性。试验结果表明:该方法在二维和三维复杂场景中均能显著提升路径规划效率,成功率和路径平滑性明显优于传统算法。研究成果可为冗余机械臂在复杂环境中的路径规划提供高效、可靠的解决方案,有助于进一步提升其在实际应用中的稳定性和适用性。
文摘针对在机器人辅助头颈部手术中双机械臂进行牵拉易产生碰撞且对目标软组织造成损伤问题,文中基于传统Informed-RRT*(Informed-Rapidly Exploring Random Tree*)路径规划算法叠加引力场降低路径搜索的盲目性。为优化传统Informed-RRT*路径规划算法存在导向性差和效率低等缺点,引入回归滤波机制避免搜索陷入局部最优,对步长进行动态调节。同时,优化了规划路径,采用冗余节点剔除策略,去除了冗余节点,提高了路径的平滑性。基于双机械臂碰撞检测方法和改进Informed-RRT*算法对双臂协调路径规划方法进行了研究。通过仿真实验可知,与原有算法相比,所提算法的迭代时间降低了72.76%,迭代次数降低了46.39%,平均路径长度约缩短6%,节点数约减少45%,验证了改进规划算法的有效性。
