• Journal of Internet of Things and Convergence
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• v.8 no.1
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• pp.59-64
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• 2022

Existing edge discovery techniques only found edges of defined shapes based on precise definitions of edges. Therefore, there are many limitations in finding edges for images of complex and diverse shapes that exist in the real world. A method for solving these problems and discovering various types of edges is a cost minimization method. In this method, the cost function and cost factor are defined and used. This cost function calculates the cost of the candidate edge model generated according to the candidate edge generation strategy. If a satisfactory result is obtained, the corresponding candidate edge model becomes the edge for the image. In this study, a new candidate edge generation strategy was proposed to discover edges for images of more diverse shapes in order to improve the disadvantage of only finding edges of a defined shape, which is a problem of the cost minimization method. In addition, the contents of improvement were confirmed through a simple simulation that reflected these points.

• Proceedings of the Korean Society of Computer Information Conference
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• 2012.07a
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• pp.87-88
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• 2012

Because intra prediction modes in H.264 are determined by the brightness continuity between neighboring blocks, they can be used as a method for extracting edge information in the compression domain. However, if we just consider 9 intra prediction modes in H.264 as 9 different edge directions, we have the following two problems. First, intra prediction modes tend to yield too many edge blocks, generating unnecessary edge information. Second, we may not need all 9 directional edges (including the DC type) in H.264 intra prediction modes. For example, the EHD (edge histogram descriptor) in MPEG-7 defines only 4 directional edge types, namely horizontal, vertical, diagonal (HVD) edges with $0^{\circ}$, $90^{\circ}$, $45^{\circ}$, and $135^{\circ}$. Here, semi-diagonal (SD) edge types with $112.5^{\circ}$, $157.5^{\circ}$, $22.5^{\circ}$, and $67.5^{\circ}$ in the intra prediction modes in H.264 are not used. In this paper. we prepose a method that removes unnecessary edges from the intra prediction modes by utilizing the total average coefficient of 4x4 blocks in each slice and assign SD edges to HVD (horizontal, vertical, diagonal, $0^{\circ}$, $90^{\circ}$, $45^{\circ}$, $135^{\circ}$) edges by the contextual information of the neighboring blocks. Experimental results show that the edges determined by the proposed method in the compression domain are comparable to those of the previous edge detection methods in the spatial domain.

• The Korean Journal of Applied Statistics
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• v.25 no.6
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• pp.1019-1026
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• 2012

This paper presents a method for the identification of "edge observations" located on a boundary area constructed by a truncation variable as well as for the identification of outliers and the after fit of multivariate skew $t$-distribution(MST) to asymmetric data. The detection of edge observation is important in data analysis because it provides information on a certain critical area in observation space. The proposed method is applied to an Australian Institute of Sport(AIS) dataset that is well known for asymmetry in data space.

• 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.

• Journal of information and communication convergence engineering
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• v.12 no.3
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• pp.193-197
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• 2014

Edges are a robust feature for object detection. In this paper, we present an edge-based background modeling method for the detection of moving objects. The edges in the image frames were mapped using robust Canny edge detector. Two edge maps were created and combined to calculate the ultimate moving-edge map. By selecting all the edge pixels of the current frame above the defined threshold of the ultimate moving edges, a temporary background-edge map was created. If the frequencies of the temporary background edge pixels for several frames were above the threshold, then those edge pixels were treated as background edge pixels. We conducted a performance comparison with previous works. The existing edge-based moving-object detection algorithms pose some difficulty due to the changes in background motion, object shape, illumination variation, and noises. The result of the performance evaluation shows that the proposed algorithm can detect moving objects efficiently in real-world scenarios.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.6
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• pp.454-458
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• 2001

Based on a fuzzy system representation of gray scale images, we derive and edge detection algorithm whose convolution kernel is different from the known kernels such as those of Robert's Prewitt's or Sobel's gradient. Our fuzzy system representation is an exact representation of the bicubic spline function which represents the gray scale image approximately. Hence the fuzzy system is a continuous function and it provides a natural way to define the gradient and the Laplacian operator. We show that the gradient at grid points can be evaluated by taking the convolution of the image with a 3$\times$3 kernel. We also that our gradient coupled with the approximate value of the continuous function generates an edge detection method which creates edge images clearer than those by other methods. A few examples of applying our methods are included.

• Journal of the Korea Society of Computer and Information
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• v.23 no.1
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• pp.25-32
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• 2018

In this paper, we propose an edge detection algorithm for auto focus of infrared camera. We designed and implemented the edge detection of infrared image by using a spatial filter on FPGA. The infrared camera should be designed to minimize the image processing time and usage of hardware resource because these days surveillance systems should have the fast response and be low size, weight and power. we applied the $3{\times}3$ mask filter which has an advantage of minimizing the usage of memory and the propagation delay to process filtering. When we applied Laplacian filter to extract contour data from an image, not only edge components but also noise components of the image were extracted by the filter. These noise components make it difficult to determine the focus state. Also a bad pixel of infrared detector causes a problem in detecting the edge components. So we propose an adaptive edge detection filter that is a method to extract only edge components except noise components of an image by analyzing a variance of pixel data in $3{\times}3$ memory area. And we can detect the bad pixel and replace it with neighboring normal pixel value when we store a pixel in $3{\times}3$ memory area for filtering calculation. The experimental result proves that the proposed method is effective to implement the edge detection for auto focus in infrared camera.

• Geomechanics and Engineering
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• v.23 no.6
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• pp.523-533
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• 2020

The current study focuses on the effect of the end shape of steel pipe piles on installation effort and bearing resistance using the pressing method of installation under dense ground conditions. The effect of pile rotation on the installation effort and bearing resistance is also investigated. The model steel piles with a flat end, cone end and cutting-edge end were used in this study. The test results indicated that cone end pile with the pressing method of installation required the least installation effort (load) and showed higher ultimate resistance than flat and cutting-edge end piles. However, pressing and rotation during cutting-edge end pile installation considerably reduces the installation effort (load and torque) if pile penetration in one rotation equal to the cutting-edge depth. Inclusion of rotation during pile installation reduces the ultimate bearing resistance. However, if penetration of the cutting-edge end pile equal to the cutting-edge depth in one rotation, the reduction in ultimate resistance can be minimized. In comparing the cone and cutting-edge end piles installed with pressing and rotation, the least installation effort is observed in the cutting-edge end pile installed with penetration rate equal to the cutting-edge depth per rotation.

• Journal of the Korean Institute of Telematics and Electronics T
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• v.36T no.4
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• pp.24-29
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• 1999

In this paper, a simple contour-preserving filtering algorithm is proposed. The goal of the contour-preserving filtering method is to remove noise ad granularity as the preprocessing for the image segmentation procedure. Our method finds edge map and separates the image into the edge region and the non-edge region using this edge map. For the non-edge region, typical smoothing filters could be used to remove the noise and the small areas during the segmentation procedure. The result of simulation shows that our method is slightly better than the typical methods such as the median filtering and gradient inverse weighted filtering in the point of view of analysis of variance (ANOVA).

• Proceedings of the KIEE Conference
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• 2006.04a
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• pp.12-14
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• 2006

This paper presents a novel scene change detection method using intra prediction mode and edge direction in H.264/AVC. When scene change occurs, there are less temporal correlation between frames, most of macro-blocks encoded in intra mode. Using this property, the method calculates the percentage of intra mode blocks in each predictive frame in order to get candidates of scene change frame. To further find scene change, we obtain edge histogram of each candidates by using eight prediction direction of intra prediction mode in H.264/AVC. We detect scene change frames with $\iota^1$-norm of edge histograms. The experimental results show that the method is efficient and robust.