The Experts below are selected from a list of 10833 Experts worldwide ranked by ideXlab platform
Kohei Arai - One of the best experts on this subject based on the ideXlab platform.
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Comparison Contour Extraction Based on Layered Structure and Fourier Descriptor on Image Retrieval
2016Co-Authors: Cahya Rahmad, Kohei AraiAbstract:Abstract—In this paper, a new content-based image retrieval technique using shape feature is proposed. A shape features extracted by layered structure representation has been implemented. The approach is extract feature shape by measuring the distance between centroid (center) and boundaries of the object that can capture multiple boundaries in the same angle, an object shape that has some points with the same angle.Once an input taking into account, the method will search most related image to the input. The correlation between input and output has been defined by specific role. Firstly the input image has to be converted from RGB image to Grayscale image and then follow by Edge Detection Process. After Edge Detection Process the boundary object will be obtained and then calculate the distance between the center of an object and the boundary of an object and put it in the feature vector and if there is another boundary on the same angle then put it in the different feature vector with a different layer. The experiment result on the plankton dataset shows that the proposed method better than other conventional Fourier descriptor method. Keywords—Cbir; Mlccd; extract features; rgb; Fourier descriptor; shape; retrieva
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comparison contour extraction based on layered structure and fourier descriptor on image retrieval
International Journal of Advanced Computer Science and Applications, 2015Co-Authors: Cahya Rahmad, Kohei AraiAbstract:In this paper, a new content-based image retrieval technique using shape feature is proposed. A shape features extracted by layered structure representation has been implemented. The approach is extract feature shape by measuring the distance between centroid (center) and boundaries of the object that can capture multiple boundaries in the same angle, an object shape that has some points with the same angle.Once an input taking into account, the method will search most related image to the input. The correlation between input and output has been defined by specific role. Firstly the input image has to be converted from RGB image to Grayscale image and then follow by Edge Detection Process. After Edge Detection Process the boundary object will be obtained and then calculate the distance between the center of an object and the boundary of an object and put it in the feature vector and if there is another boundary on the same angle then put it in the different feature vector with a different layer. The experiment result on the plankton dataset shows that the proposed method better than other conventional Fourier descriptor method.
A G Constantinides - One of the best experts on this subject based on the ideXlab platform.
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robust ellipse fitting based on lagrange programming neural network and locally competitive algorithm
Neurocomputing, 2020Co-Authors: Zhanglei Shi, Hao Wang, Chising Leung, Junli Liang, Kim Fung Tsang, A G ConstantinidesAbstract:Abstract Given a set of 2-dimensional (2D) scattering points, obtained from the Edge Detection Process, the aim of ellipse fitting is to construct an elliptic equation that best fits the scattering points. However, the 2D scattering points may contain some outliers. To address this issue, we devise a robust ellipse fitting approach based on two analog neural network models, Lagrange programming neural network (LPNN) and locally competitive algorithm (LCA). We formulate the fitting task as a nonsmooth constrained optimization problem, in which the objective function is an approximated l0-norm term. As the LPNN model cannot handle non-differentiable functions, we utilize the internal state concept of LCA to avoid the computation of the derivative at non-differentiable points. Simulation results show that the proposed ellipse fitting approach is superior to several state-of-the-art algorithms.
Cahya Rahmad - One of the best experts on this subject based on the ideXlab platform.
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Comparison Contour Extraction Based on Layered Structure and Fourier Descriptor on Image Retrieval
2016Co-Authors: Cahya Rahmad, Kohei AraiAbstract:Abstract—In this paper, a new content-based image retrieval technique using shape feature is proposed. A shape features extracted by layered structure representation has been implemented. The approach is extract feature shape by measuring the distance between centroid (center) and boundaries of the object that can capture multiple boundaries in the same angle, an object shape that has some points with the same angle.Once an input taking into account, the method will search most related image to the input. The correlation between input and output has been defined by specific role. Firstly the input image has to be converted from RGB image to Grayscale image and then follow by Edge Detection Process. After Edge Detection Process the boundary object will be obtained and then calculate the distance between the center of an object and the boundary of an object and put it in the feature vector and if there is another boundary on the same angle then put it in the different feature vector with a different layer. The experiment result on the plankton dataset shows that the proposed method better than other conventional Fourier descriptor method. Keywords—Cbir; Mlccd; extract features; rgb; Fourier descriptor; shape; retrieva
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comparison contour extraction based on layered structure and fourier descriptor on image retrieval
International Journal of Advanced Computer Science and Applications, 2015Co-Authors: Cahya Rahmad, Kohei AraiAbstract:In this paper, a new content-based image retrieval technique using shape feature is proposed. A shape features extracted by layered structure representation has been implemented. The approach is extract feature shape by measuring the distance between centroid (center) and boundaries of the object that can capture multiple boundaries in the same angle, an object shape that has some points with the same angle.Once an input taking into account, the method will search most related image to the input. The correlation between input and output has been defined by specific role. Firstly the input image has to be converted from RGB image to Grayscale image and then follow by Edge Detection Process. After Edge Detection Process the boundary object will be obtained and then calculate the distance between the center of an object and the boundary of an object and put it in the feature vector and if there is another boundary on the same angle then put it in the different feature vector with a different layer. The experiment result on the plankton dataset shows that the proposed method better than other conventional Fourier descriptor method.
Zhanglei Shi - One of the best experts on this subject based on the ideXlab platform.
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robust ellipse fitting based on lagrange programming neural network and locally competitive algorithm
Neurocomputing, 2020Co-Authors: Zhanglei Shi, Hao Wang, Chising Leung, Junli Liang, Kim Fung Tsang, A G ConstantinidesAbstract:Abstract Given a set of 2-dimensional (2D) scattering points, obtained from the Edge Detection Process, the aim of ellipse fitting is to construct an elliptic equation that best fits the scattering points. However, the 2D scattering points may contain some outliers. To address this issue, we devise a robust ellipse fitting approach based on two analog neural network models, Lagrange programming neural network (LPNN) and locally competitive algorithm (LCA). We formulate the fitting task as a nonsmooth constrained optimization problem, in which the objective function is an approximated l0-norm term. As the LPNN model cannot handle non-differentiable functions, we utilize the internal state concept of LCA to avoid the computation of the derivative at non-differentiable points. Simulation results show that the proposed ellipse fitting approach is superior to several state-of-the-art algorithms.
H Seki - One of the best experts on this subject based on the ideXlab platform.
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a color cmos imager with 4 times 4 white rgb color filter array for increased low illumination signal to noise ratio
IEEE Transactions on Electron Devices, 2009Co-Authors: Hironaga Honda, Yoshinori Iida, Yoshitaka Egawa, H SekiAbstract:A color CMOS image sensor with the 4 times 4 White-RGB color filter array (CFA) including 50% white pixels has been developed. A transparent layer has been fabricated on the white pixel to realize over 95% transmission for visible light with wavelengths of 400-700 nm. Pixel pitch and number of the pixels were 3.3 mum and 2 million, respectively. With the simple and low-noise color separation Process, low-illumination signal-to-noise ratios of luminance signal have been increased by 6 dB, compared with those of the Bayer pattern. Moreover, by locating the pixels so that every color components can be detected in every column and line, color artifacts at the Edge were suppressed. The Edge Detection Process became unnecessary and the Process time was reduced by 70%. The new CFA has the potential to significantly increase the sensitivity of CMOS/CCD image sensors.
