• International journal of advanced smart convergence
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• v.13 no.2
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• pp.88-93
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• 2024

The main problem with existing edge detection techniques is that they have many limitations in detecting edges for complex and diverse images that exist in the real world. This is because only edges of a defined shape are discovered based on an accurate definition of the edge. One of the methods to solve this problem is the cost minimization method. In the cost minimization method, cost elements and cost functions are defined and used. The cost function calculates the cost for the candidate edge model generated according to the candidate edge generation strategy, and if the cost is found to be satisfactory, the candidate edge model becomes the edge for the image. In this study, we proposed an enhanced candidate edge generation strategy to discover edges for more diverse types of images in order to improve the shortcoming of the cost minimization method, which is that it only discovers edges of a defined type. As a result, improved edge detection results were confirmed.

• Proceedings of the IEEK Conference
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• 2000.11d
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• pp.41-44
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• 2000

In this paper, we present an efficient face detection algorithm for locating vertical views of human faces in complex scenes. The algorithm models the distribution of human skin color in YCbCr color space and find various ace candidate regions. Face candidate regions are found by thresholding with predetermined thresholds. For each of these face candidate regions, The sobel edge operator is used to find edge regions. For each edge region, we used an ellipse detection algorithm which is similar to hough transform to refine the candidate region. Finally if a substantial number of he facial features (eye, mouth) are found successfully in the candidate region, we determine he ace candidate region as a face region. e show empirically that the presented algorithm an find the face region very well in the complex scenes.

• Proceedings of the IEEK Conference
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• 2003.11a
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• pp.233-236
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• 2003

In this paper, we propose a method to search the evidence such as a knife found in the crime scene based on GHT from an image database Such objects like knives are simitar in shape. The proposed method utilizes the small shape differences among objects as much as possible to distinguish an object from similar shaped objects. It consists of the GHT based candidate generation and top-down candidate verification. For the fast generation of the candidate 1ist, the GHT operation is performed un the down sampled edge list. The test results show that it can retrieve the correct object even with a pan of object in reasonable time.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.23 no.59
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• pp.1-10
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• 2000

The traveling salesman problem is a representative NP-Complete problem. It needs lots of time to get a solution as the number of city increase. So, we need an efficient heuristic algorithm that gets good solution in a short time. Almost edges that participate in optimal path have somewhat low value cost. This paper discusses the property of nearest neighbor and 3-opt. This paper uses nearest neighbor's property to select candidate edge. Candidate edge is a set of edge that has high probability to improve cycle path. We insert edge that is one of candidate edge into intial cycle path. As two cities are connected. It does not satisfy hamiltonian cycle's rule that every city must be visited and departed only one time. This paper uses 3-opt's method to sustain hamiltonian cycle while inserting edge into cycle path. This paper presents a highly efficient heuristic algorithm verified by numerous experiments.

• 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.

• International journal of advanced smart convergence
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• v.11 no.4
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• pp.47-56
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• 2022

Recently bank card number recognition plays an important role in improving payment efficiency. In this paper we propose a new bank-card number identification algorithm. The proposed algorithm consists of three modules which include edge detection, candidate region generation, and recognition. The module of 'edge detection' is used to obtain the possible digital region. The module of 'candidate region generation' has the role to expand the length of the digital region to obtain the candidate card number regions, i.e. to obtain the final bank card number location. And the module of 'recognition' has Convolutional deep learning Neural Network (CNN) to identify the final bank card numbers. Experimental results show that the identification rate of the proposed algorithm is 95% for the card numbers, which shows 20% better than that of conventional algorithm or method.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2013.06a
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• pp.180-181
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• 2013

High efficiency video coding (HEVC) appears due to the demand on high compression video coding beyond H.264/AVC in ultra-high definition (UHD) videos. As for intra prediction, HEVC has 35 prediction modes while H.264/AVC has 9 intra modes. To exploit the spatial correlation, we adopt an edge detection method, establish the edge map, and adaptively select the candidate modes using the acquired edge information in a block. The number of the candidate modes is determined through trade-off between computational complexity and coding efficiency. Besides, the range of coding unit sizes is determined using the uniqueness of the edge directions for the given image block. As a result, we reduced the encoding time by 56.8% at the cost of 2.5% BD-BR increase on average compared to Full modes at the HEVC reference software (HM 6.0 [1]).

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.3
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• pp.847-852
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• 2010

Text in natural images has a various and important feature of image. Therefore, to detect text and extraction of text, recognizing it is a studied as an important research area. Lately, many applications of various fields is being developed based on mobile phone camera technology. Detecting edge component form gray-scale image and detect an boundary of text regions by local standard deviation and get an connected components using Euclidean distance of RGB color space. Labeling the detected edges and connected component and get bounding boxes each regions. Candidate of text achieved with heuristic rule of text. Detected candidate text regions was merged for generation for one candidate text region, then text region detected with verifying candidate text region using ectilarity characterization of adjacency and ectilarity between candidate text regions. Experctental results, We improved text region detection rate using completentary of edge and color connected component.

• Journal of the Korean Society for Precision Engineering
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• v.18 no.3
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• pp.68-74
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• 2001

A lane detection based on a road model or feature all needs correct acquirement of information on the lane in an image. It is inefficient to implement a lane detection algorithm through the full range of an image when it is applied to a real road in real time because of the calculating time. This paper defines two (other proper terms including"modes") for detecting lanes on a road. First is searching mode that is searching the lane without any prior information of a road. Second is recognition mode, which is able to reduce the size and change the position of a searching range by predicting the position of a lane through the acquired information in a previous frame. It allows to extract accurately and efficiently the edge candidate points of a lane without any unnecessary searching. By means of inverse perspective transform which removes the perspective effect on the edge candidate points, we transform the edge candidate information in the Image Coordinate System(ICS) into the plan-view image in the World Coordinate System(WCS). We define a linear approximation filter and remove faulty edge candidate points by using it. This paper aims at approximating more correctly the lane of an actual road by applying the least-mean square method with the fault-removed edge information for curve fitting.e fitting.

• Journal of Korea Entertainment Industry Association
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• v.5 no.1
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• pp.127-133
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• 2011

Characters in natural image are an important information with various context. Previous work of character region detection algorithms is not detect of character region in case of image complexity and the surrounding lighting, similar background to character, so this paper propose an method of character region detection in natural image using edge and connected component by morphological reconstructions. Firstly, we detect edge using Canny-edge detector and connected component with local min/max value by morphological reconstructed-operation in gray-scale image, and labeling each of detected connected component elements. lastly, detected candidate of text regions was merged for generation for one candidate text region, Final text region detected by checking the similarity and adjacency of neighbor of text candidate individual character. As the results of experiments, proposed algorithm improved the correctness of character regions detection using edge and connected components.