In this paper,we consider the Chan–Vese(C-V)model for image segmentation and obtain its numerical solution accurately and efficiently.For this purpose,we present a local radial basis function method based on a Gaussi...In this paper,we consider the Chan–Vese(C-V)model for image segmentation and obtain its numerical solution accurately and efficiently.For this purpose,we present a local radial basis function method based on a Gaussian kernel(GA-LRBF)for spatial discretization.Compared to the standard radial basis functionmethod,this approach consumes less CPU time and maintains good stability because it uses only a small subset of points in the whole computational domain.Additionally,since the Gaussian function has the property of dimensional separation,the GA-LRBF method is suitable for dealing with isotropic images.Finally,a numerical scheme that couples GA-LRBF with the fourth-order Runge–Kutta method is applied to the C-V model,and a comparison of some numerical results demonstrates that this scheme achieves much more reliable image segmentation.展开更多
对基于熔化极气体保护(Metal active gas,MAG)焊的管道打底焊过程中通过电荷耦合器件(Charge-coupled device,CCD)摄像机与复合滤光技术组成的光学系统实时采集的正面焊缝区域图像进行分析,实现熔池图像边缘提取。对获取的焊缝区域图像...对基于熔化极气体保护(Metal active gas,MAG)焊的管道打底焊过程中通过电荷耦合器件(Charge-coupled device,CCD)摄像机与复合滤光技术组成的光学系统实时采集的正面焊缝区域图像进行分析,实现熔池图像边缘提取。对获取的焊缝区域图像,经过高斯平滑后,考虑到初始轮廓对Chan-Vese主动轮廓模型提取边缘效率的影响,使用基于阈值的方法,用矩形标出熔池的初始区域,即实现熔池区域粗定位,求取矩形区域中心,以该中心设定一椭圆作为熔池初始轮廓,使用Chan-Vese主动轮廓模型提取熔池边缘。借助该模型实现了MAG管道打底焊焊缝区域图像的熔池边缘提取,与Sobel变换等方法比较,该方法提高了熔池边缘提取的精度。展开更多
To improve the segmentation precision of overlapping crop leaves,this paper presents an effective image segmentation method based on the Chan–Vese model and Sobel operator.The approach consists of three stages.First,...To improve the segmentation precision of overlapping crop leaves,this paper presents an effective image segmentation method based on the Chan–Vese model and Sobel operator.The approach consists of three stages.First,a feature that identifies hues with relatively high levels of green is used to extract the region of leaves and remove the background.Second,the Chan–Vese model and improved Sobel operator are implemented to extract the leaf contours and detect the edges,respectively.Third,a target leaf with a complex background and overlapping is extracted by combining the results obtained by the Chan–Vese model and Sobel operator.To verify the effectiveness of the proposed algorithm,a segmentation experiment was performed on 30 images of cucumber leaf.The mean error rate of the proposed method is 0.0428,which is a decrease of 6.54%compared with the mean error rate of the level set method.Experimental results show that the proposed method can accurately extract the target leaf from cucumber leaf images with complex backgrounds and overlapping regions.展开更多
基金sponsored by Guangdong Basic and Applied Basic Research Foundation under Grant No.2021A1515110680Guangzhou Basic and Applied Basic Research under Grant No.202102020340.
文摘In this paper,we consider the Chan–Vese(C-V)model for image segmentation and obtain its numerical solution accurately and efficiently.For this purpose,we present a local radial basis function method based on a Gaussian kernel(GA-LRBF)for spatial discretization.Compared to the standard radial basis functionmethod,this approach consumes less CPU time and maintains good stability because it uses only a small subset of points in the whole computational domain.Additionally,since the Gaussian function has the property of dimensional separation,the GA-LRBF method is suitable for dealing with isotropic images.Finally,a numerical scheme that couples GA-LRBF with the fourth-order Runge–Kutta method is applied to the C-V model,and a comparison of some numerical results demonstrates that this scheme achieves much more reliable image segmentation.
文摘对基于熔化极气体保护(Metal active gas,MAG)焊的管道打底焊过程中通过电荷耦合器件(Charge-coupled device,CCD)摄像机与复合滤光技术组成的光学系统实时采集的正面焊缝区域图像进行分析,实现熔池图像边缘提取。对获取的焊缝区域图像,经过高斯平滑后,考虑到初始轮廓对Chan-Vese主动轮廓模型提取边缘效率的影响,使用基于阈值的方法,用矩形标出熔池的初始区域,即实现熔池区域粗定位,求取矩形区域中心,以该中心设定一椭圆作为熔池初始轮廓,使用Chan-Vese主动轮廓模型提取熔池边缘。借助该模型实现了MAG管道打底焊焊缝区域图像的熔池边缘提取,与Sobel变换等方法比较,该方法提高了熔池边缘提取的精度。
基金This study was supported by the National Natural Science Foundation of China(No.61403035)Natural Science Foundation of Beijing Municipality(No.9152009)Science and Technology Innovation Ability Construction Project of Beijing Academy of Agriculture and Forestry Science(No.KJCX20170206).
文摘To improve the segmentation precision of overlapping crop leaves,this paper presents an effective image segmentation method based on the Chan–Vese model and Sobel operator.The approach consists of three stages.First,a feature that identifies hues with relatively high levels of green is used to extract the region of leaves and remove the background.Second,the Chan–Vese model and improved Sobel operator are implemented to extract the leaf contours and detect the edges,respectively.Third,a target leaf with a complex background and overlapping is extracted by combining the results obtained by the Chan–Vese model and Sobel operator.To verify the effectiveness of the proposed algorithm,a segmentation experiment was performed on 30 images of cucumber leaf.The mean error rate of the proposed method is 0.0428,which is a decrease of 6.54%compared with the mean error rate of the level set method.Experimental results show that the proposed method can accurately extract the target leaf from cucumber leaf images with complex backgrounds and overlapping regions.
