• Journal of KIISE:Software and Applications
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• v.34 no.11
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• pp.961-970
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• 2007

Since the usual conventional edge detectors employ the local differential derivatives, the detected edges are not uniform in their widths or some edges are missed out of the detection on magnified images. We employ a mapping from the exactly monotonic intensity distributions of ramp edges to the simple step functions of intensity, which is referred to as perfect sharpening map of ramp edges. This map is based on the non-local feature of intensity distribution and used to introduce a modified differentiation, in terms of which we can construct an efficient edge detector adaptive to the variation of edge width. By adopting the operator MADD in this paper, we developed an edge detector that works stably against the magnification of image or the variation of edge width. It is shown by comparing to the conventional algorithms that the proposed one is very excellent.

• Journal of IKEEE
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• v.19 no.1
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• pp.10-17
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• 2015

In this Paper, an efficient method of FPGA based design and implementation of Sobel Edge detector block using 3-Line buffers is presented. The FPGA provides the proper and sufficient hardware for image processing algorithms with flexibility to support Sobel edge detection algorithm. A pipe-lined method is used to implement the edge detector. The proposed Sobel edge detection operator is an model using of Finite State Machine(FSM) which executes a matrix mask operation to determine the level of edge intensity through different of pixels on an image. This approach is useful to improve the system performance by taking advantage of efficient look up tables, flip-flop resources on target device. The proposed Sobel detector using 3-line buffers is synthesized with Xilinx ISE 14.2 and implemented on Virtex II xc2vp-30-7-FF896 FPGA device. Using matlab, we show better PSNR performance of proposed design in terms of 3-Line buffers utilization.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.17 no.7
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• pp.709-718
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• 1992

A new edge detection operator superimposing two displaced Gaussian smoothing filters Is proposed as an approximate operator for the DroG(flrst derivative of Gaussian) known as a sub-op-timal step edge detector. The performance of the proposed edge detector Is very close to that of the DroG with the performance criteria . signal to noise ratio, locality, and multiple response. And the computational complexity can be reduced almost by a half of that of DroG, because of the use of common 2-D smoothing filter for DroG and LoG ( Laplacian of Gausslan) systems.

• Nuclear Engineering and Technology
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• v.54 no.11
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• pp.4220-4225
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• 2022

Monte Carlo (MC) simulations are increasingly being used as an alternative or supplement to the gamma spectrometric method in determining the full energy peak efficiency (FEPE) necessary for radionuclide identification and quantification. The MC method is more advantageous than the experimental method in terms of both cost and time. Experimental calibration with standard sources is difficult, especially for specimens with unusually shaped geometries. However, with MC, efficiency values can be obtained by modeling the geometry as desired without using any calibration source. Modeling the detector with the correct parameters is critical in the MC method. These parameters given to the user by the manufacturer are especially the dimensions of the crystal and its front edge, the thickness of the dead layer, dimensions, and materials of the detector components. This study aimed to investigate the effect of the front edge geometry of the detector crystal on efficiency, so the effect of rounded and sharp modeled front edges on the FEPE was investigated for <300 keV with three different HPGe detectors in point and volume source geometries using PHITS MC code. All results showed that the crystal should be modeled as a rounded edge, especially for gamma-ray energies below 100 keV.

• Nuclear Engineering and Technology
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• v.56 no.6
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• pp.2057-2062
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• 2024

Among photon counting detector (PCD)-based technologies, the K-edge subtraction (KES) method has a very high material decomposition efficiency. Yet, since the increase in noise in the X-ray image to which the KES method is applied is inevitable, research on image quality improvement is essential. Here, we modeled a block-matching and 3D filtering (BM3D) algorithm and applied it to PCD-based X-ray images with the improved KES (IKES) method. For PCD modeling, Monte Carlo simulation was used, and a phantom composed of iodine substances with different concentrations was designed. The IKES method was modeled by adding a log term to KES, and the X-ray image used for subtraction was obtained by applying the 3.0 keV range based on the K-edge region of iodine. As a result, the IKES image using the BM3D algorithm showed the lowest normalized noise power spectrum value. In addition, we confirmed that the contrast-to-noise ratio and no-reference-based evaluation results when the BM3D algorithm was applied to the IKES image were improved by 29.36 % and 20.56 %, respectively, compared to the noisy image. In conclusion, we demonstrated that the IKES imaging technique using a PCD-based detector and the BM3D algorithm fusion technique were very efficient for X-ray imaging.

• The Korean Journal of Applied Statistics
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• v.24 no.2
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• pp.359-371
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• 2011

Edge detection in images is an important step in image segmentation and object recognition as preprocessing for image processing. This paper presents a new edge detection using local histogram analysis based on wavelet transform. In this work, the wavelet transform uses three components (horizontal, vertical and diagonal) to find the magnitude of the gradient vector, instead of the conventional approach in which tw components are used. We compare the magnitude of the gradient vector with the threshold that is obtained from a local histogram analysis to conclude that an edge is present or not. Some experimental results for our edge detector with a Sobel, Canny, Scale Multiplication, and Mallat edge detectors on sample images are given and the performances of these edge detectors are compared in terms of quantitative and qualitative measures. Our detector performs better than the other wavelet-based detectors such as Scale Multiplication and Mallat detectors. Our edge detector also preserves a good performance even if the Sobel and Canny detector are sharply low when the images are highly corrupted.

• Proceedings of the IEEK Conference
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• 2001.06d
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• pp.85-88
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• 2001

Unlike rigid objects or This paper developes the algorithm for segmenting gaseous objects on an image plane. Unlike rigid objects or solid non-rigid objects, gaseous objects vary in density even within single-object regions and the edge intensity differs at different locations. So, an edge detector may detect only strong edges and detected edges may be an incomplete parts of an whole object's boundary. Due to this property of gaseous objects, it is not easy to distinguish the real edges of gaseous objects from the noisy-like edges such as leaves. Our algorithm uses two criteria of edge intensity and edge's line connectivity, then applies fuzzy set so as to obtain the proper threshold of the edge detector

• Convergence Security Journal
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• v.6 no.3
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• pp.29-35
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• 2006

In order to detect and locate edge features precisely in real images we have developed an algorithm by introducing a nonlocal differentiation of intensity profiles called adaptive directional derivative (ADD), which is evaluated independently of varying ramp widths. In this paper, we first develop the edge detector system employing the ADD and then, the performance of the algorithm is illustrated by comparing the results to those from the Canny's edge detector.

• Convergence Security Journal
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• v.7 no.3
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• pp.39-44
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• 2007

In order to detect and locate edge features precisely in real images we have developed an algorithm by introducing a nonlocal differentiation of intensity profiles called adaptive directional derivative (ADD), which is evaluated independently of varying ramp widths. In this paper, we first develop the edge detector system employing the ADD and then, the performance of the algorithm is illustrated by comparing the results to those from the Canny's edge detector.

• The Journal of the Korea Contents Association
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• v.13 no.10
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• pp.54-61
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• 2013

This paper presents a simplified hardware based edge detection circuit which is based on an analog correlator combining with the neighbor pixels in CMOS image sensor. A pixel element of the edge detector consists of an active pixel sensor and an analog correlator circuit which connects two neighbor pixels. The edge detector shares a comparator on each column that the comparator decides an edge of the target pixel with an adjustable reference voltage. The circuit detects image edge from CIS directly that reduces area and power consumption 4 times and 20%, respectively, compared with the previous works. And also it has advantage to regulate sensitivity of the edge detection because the threshold value is able to control externally. The fabricated chip has 34% of fill factor and 0.9 ${\mu}W$ of power per a pixel under 0.18 ${\mu}m$ CMOS technology.