工业设计加工产品零件视角精度优化控制
|
摘要
在工业设计中对加工产品零件的视角精度控制问题的优化,能够有效提高产品零件的精密度。对零件精度的优化控制,需要计算图像视觉显著性的显著图,分割出加工视角并加以标记,完成对加工产品零件的精度控制。传统方法构造直方图以获取零件加工视角表示,给出视角控制阈值,但忽略了对加工视角的及时标记,导致控制效果不理想。提出基于视觉注意机制的工业设计加工产品零件视角精度优化控制方法。对加工视频帧进行颜色空间转换,提取零件加工视角特征,计算加工视角特征显著性,给出加工产品零件加工视频帧的幅度谱,计算出视觉显著性的显著图,将加工视角分割出来并加以标记,计算零件加工视角模板与候选零件加工视角之间的距离,完成对工业设计加工产品零件视角精度优化控制。仿真证明,所提方法视角控制精度高,可以有效地提高对加工产品零件的设计质量。
This paper proposes a method for optimization control of visual angle accuracy of processed product of industrial design based on visual attention machining.Firstly,the color spatial switching for processing video frame is carried out and feature of visual angle of part processing is extracted,and then significance of the feature is calculated.Moreover,the magnitude spectra of video frame of the part processing is provided,and video significant map is worked out.The processing visual angle is partitioned to mark it,and distance between the visual angle template and candidate visual angle is calculated.Finally,the optimization control is accomplished.Simulation results show that the method has high control accuracy of video angle.It can enhance design quality of processed product part efficiently.
This paper proposes a method for optimization control of visual angle accuracy of processed product of industrial design based on visual attention machining.Firstly,the color spatial switching for processing video frame is carried out and feature of visual angle of part processing is extracted,and then significance of the feature is calculated.Moreover,the magnitude spectra of video frame of the part processing is provided,and video significant map is worked out.The processing visual angle is partitioned to mark it,and distance between the visual angle template and candidate visual angle is calculated.Finally,the optimization control is accomplished.Simulation results show that the method has high control accuracy of video angle.It can enhance design quality of processed product part efficiently.
引文
[1]周庆.复杂工业设计产品外观轮廓合理性的模型研究[J].计算机仿真,2015,32(4):355-358.
[2]戴知圣,潘晴,钟小芸.基于机器视觉的工件尺寸和角度的测量[J].计算机测量与控制,2016,24(2):27-29.
[3]赵琳,等.基于HASH感知和小波变换的目标跟踪算法[J].系统工程与电子技术,2016,38(4):739-745.
[4]李子印,朱明凌,陈柱.融合图像感知哈希技术的运动目标跟踪[J].中国图象图形学报,2015,20(6):795-804.
[5]宗薇.一种新的视频流动态目标跟踪算法[J].计算机应用研究,2015,32(3):954-956.
[6]刘超,惠晶.基于改进CAMShift的运动目标跟踪算法[J].计算机工程与应用,2014,50(11):149-153.
[7]马正华,顾苏杭,戎海龙.基于SIFT特征匹配的Cam Shift运动目标跟踪算法[J].计算机科学,2014,41(6):291-294.
[8]陈定坤,杨艳.一种改进的Mean-shift运动目标跟踪算法[J].半导体光电,2015,36(1):160-164.
[9]江二华,王汇源.一种改进的运动目标跟踪算法[J].计算机工程与应用,2015,51(22):168-171.
[10]夏天维,侯翔.基于自适应Kalman滤波的机器人运动目标跟踪算法[J].计算机测量与控制,2015,23(1):173-175.
[2]戴知圣,潘晴,钟小芸.基于机器视觉的工件尺寸和角度的测量[J].计算机测量与控制,2016,24(2):27-29.
[3]赵琳,等.基于HASH感知和小波变换的目标跟踪算法[J].系统工程与电子技术,2016,38(4):739-745.
[4]李子印,朱明凌,陈柱.融合图像感知哈希技术的运动目标跟踪[J].中国图象图形学报,2015,20(6):795-804.
[5]宗薇.一种新的视频流动态目标跟踪算法[J].计算机应用研究,2015,32(3):954-956.
[6]刘超,惠晶.基于改进CAMShift的运动目标跟踪算法[J].计算机工程与应用,2014,50(11):149-153.
[7]马正华,顾苏杭,戎海龙.基于SIFT特征匹配的Cam Shift运动目标跟踪算法[J].计算机科学,2014,41(6):291-294.
[8]陈定坤,杨艳.一种改进的Mean-shift运动目标跟踪算法[J].半导体光电,2015,36(1):160-164.
[9]江二华,王汇源.一种改进的运动目标跟踪算法[J].计算机工程与应用,2015,51(22):168-171.
[10]夏天维,侯翔.基于自适应Kalman滤波的机器人运动目标跟踪算法[J].计算机测量与控制,2015,23(1):173-175.