• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2003.06a
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• pp.90-93
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• 2003

본 논문에서는 watershed segmentation을 이용해서 특정한 영역의 윤곽선정보를 바탕으로 영상을 추출하는 방법을 제안한다. 일반적인 영역추출방법에는 폐곡선을 이용한 영역추출방법, snake라 불리는 에너지 최소화를 이용한 active contour 방법 등 여러 가지가 있다. 특히 이러한 방법에는 연산시간이 많이 걸린다는 단점이 있는데, 이러한 문제점을 해결하기 위해서 canny edge detector를 사용하여 윤곽선 정보를 추출하고, watershed segmentation으로 영상을 분할한 후 윤곽선 정보를 이용해서 특정 영상의 영역에 대한 부분을 병합하여 추출하는 방법을 사용하였다. 제안하는 방법으로 기존의 추출 방법중의 하나인 levelset과 비교 실험한 결과 거의 동일한 영상의 결과를 얻을 수 있었으며 연산시간을 줄일 수 있었다.

• Proceedings of the Korean Information Science Society Conference
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• 2000.10b
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• pp.449-451
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• 2000

본 논문에서는 내용기반 이미지 검색 시스템을 제작하기 위하여 필수적으로 선행되어야 하는 이미지의 영역구분에 대한 새로운 방법인 경계값을 이용한 영역추출 방법을 소개한다. 빠르고 정확한 이미지 검색엔진을 구현하기 위하여 질의의 결과가 될 이미지들은 전처리기에 의하여 모든 영역을 추출한 뒤 각각의 영역에 따른 특성(feature)를 저장하고 있어야 한다. 정확한 질의 결과를 얻기 위하여는 정확히 영역을 추출할 수 있고 그 특성도 추출할 수 있는 전처리기를 사용하여야 한다. 또한 정확도만을 중시하여 너무 복잡한 알고리즘을 사용한다면 그 또한 실용적이지 못하게 된다. 경계값을 이용한 영역추출 방법은 이미지의 각 점에 대한 경계값(edge value)을 이용하여 그 경계값이 작은 점으로부터 시작하여 경계값이 큰 점들을 병합해 가면서 인접한 영역간의 크기, 색상 등을 고려하여 각각의 영역을 구분해 낸다. 이 방법의 가장 큰 특징은 텍스쳐(texture)를 제외한 일반적인 영역뿐 아니라 텍스쳐 포함하는 영역도 추출할 수 있는 점과 빠른 처리 속도에 있다.

• Science of Emotion and Sensibility
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• v.7 no.4
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• pp.57-65
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• 2004

In this paper, we present a hybrid approach which incorporates color, texture and shape in content-based image retrieval. Colors in each image are clustered into a small number of representative colors. The feature descriptor consists of the representative colors and their percentages in the image. A similarity measure similar to the cumulative color histogram distance measure is defined for this descriptor. The co-occurrence matrix as a statistical method is used for texture analysis. An optimal set of five statistical functions are extracted from the co-occurrence matrix of each image, in order to render the feature vector for eachimage maximally informative. The edge information captured within edge histograms is extracted after a pre-processing phase that performs color transformation, quantization, and filtering. The features where thus extracted and stored within feature vectors and were later compared with an intersection-based method. The content-based retrieval system is tested to be effective in terms of retrieval and scalability through experimental results and precision-recall analysis.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.5
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• pp.786-792
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• 2022

In the cloud computing environment, there has been a lot of research into the Fog/Edge Computing (FEC) paradigm for securing user proximity of application services and computation offloading to alleviate service delay difficulties. The method of predicting dynamic location change patterns of edge devices (moving objects) requesting application services is critical in this FEC environment for efficient computing resource distribution and deployment. This paper proposes an optimal moving pattern extraction algorithm in which variable weights (distance, time, congestion) are applied to selected paths in addition to a support factor threshold for frequency patterns (moving objects) of edge devices. The proposed algorithm is compared to the OPE_freq [8] algorithm, which just applies frequency, as well as the A^* and Dijkstra algorithms, and it can be shown that the execution time and number of nodes accessed are reduced, and a more accurate path is extracted through experiments.

• Journal of the Korea Society of Computer and Information
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• v.6 no.2
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• pp.14-19
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• 2001

This paper presents a new method of 3-D surface feature extraction using a quadratic pol expression. With a range image, we get an edge map through the modified scan line technique this edge map, we label a 3-dimensional object to divide object's region and extract cent corner points from it's region. Then we determine whether the segmented region is a planar or a curved from the quadric surface equation. we calculate the coefficients of the planar su the curved surface to represent regions. In this article. we prove performance of the metho synthetic and real (Odetics) range images.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.625-627
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• 2022

FEC (Fog/Edge Computing) 환경에서 지연시간 최소화는 로컬과 에지 서버 간의 효율적인 협력을 보장하기 위한 최적의 계산 오프로딩 방법 결정을 통해 실현될 수 있다. 본 논문은 임의의 응용 서비스 실행모듈에 대한 부분 오프로딩 기반의 로컬(모바일 장치)과 에지 서버 간의 협업 경로를 추출하는 방법을 제안한다. 제안 방법은 다중 분기구조를 포함하는 응용 서비스 실행모듈에 대한 부분 오프로딩 기반의 최적 협업 실행경로 추출 방법을 제안한다. 제안 방법은 각 부분 모듈들의 실행위치에 따라 변화되는 지연시간 측정 및 분석에 적용가능하다.

• Journal of the Korea Society of Computer and Information
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• v.17 no.3
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• pp.51-57
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• 2012

Hepatic vessel tree is the key structure for hepatic disease diagnosis and liver surgery planning. Especially, it is used to evaluate the donors' and recipients' liver for the LDLT(Living Donors Liver Transplantation) and estimate the volumes of left and right hepatic lobes for securing their life in the LDLT. In this study, we propose a method to apply canny edge detection that is not affected by noise to the liver images for automatic segmentation of hepatic vessels tree in contrast abdominal MDCT image. Using histograms and average pixel values of the various liver CT images, optimized parameters of the Canny algorithm are determined. It is more time-efficient to use the common parameters than to change parameters manually according to CT images. Candidates of hepatic vessels are extracted by threshold filtering around the detected the vessel edge. Finally, using a system which detects the true-negatives and the false-positives in horizontal and vertical direction, the true-negatives are added in candidate of hepatic vessels and the false-positives are removed. As a result of the process, the various hepatic vessel trees of patients are accurately reconstructed in 3D.

• Korean Journal of Remote Sensing
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• v.40 no.4
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• pp.387-396
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• 2024

Most very high-resolution (VHR) satellite images provide rational polynomial coefficients (RPC) data to facilitate the transformation between ground coordinates and image coordinates. However, initial RPC often contains geometric errors, necessitating correction through matching with ground control points (GCPs). A GCP chip is a small image patch extracted from an orthorectified image together with height information of the center point, which can be directly used for geometric correction. Many studies have focused on area-based matching methods to accurately align GCP chips with VHR satellite images. In cases with seasonal differences or changed areas, edge-based algorithms are often used for matching due to the difficulty of relying solely on pixel values. However, traditional edge extraction algorithms,such as canny edge detectors, require appropriate threshold settings tailored to the spectral characteristics of satellite images. Therefore, this study utilizes deep learning-based edge information that is insensitive to the regional characteristics of satellite images for matching. Specifically,we use a pretrained pixel difference network (PiDiNet) to generate the edge maps for both satellite images and GCP chips. These edge maps are then used as input for normalized cross-correlation (NCC) and relative edge cross-correlation (RECC) to identify the peak points with the highest correlation between the two edge maps. To remove mismatched pairs and thus obtain the bias-compensated RPC, we iteratively apply the data snooping. Finally, we compare the results qualitatively and quantitatively with those obtained from traditional NCC and RECC methods. The PiDiNet network approach achieved high matching accuracy with root mean square error (RMSE) values ranging from 0.3 to 0.9 pixels. However, the PiDiNet-generated edges were thicker compared to those from the canny method, leading to slightly lower registration accuracy in some images. Nevertheless, PiDiNet consistently produced characteristic edge information, allowing for successful matching even in challenging regions. This study demonstrates that improving the robustness of edge-based registration methods can facilitate effective registration across diverse regions.

• Proceedings of the Korea Information Processing Society Conference
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• 2000.10a
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• pp.317-320
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• 2000

경계선 추출(edge detection)은 영상처리 및 인식 분야의 여러 응용분야에서 쓰이는 중요한 전처리과정 중 하나이다. 경계선이란 두 영역 사이의 경계에 위치한 점들[12]을 말하며, 경계선 추출은 영상 속에 위치한 이런 경계점들을 찾는 작업을 말한다. 경계선 추출 방법에 대해서는 많은 연구가 진행되어 왔지만 사물의 실제 경계선을 정확히 추출해내는 것은 여전히 어려운 문제로 남아있다. 이에 본 논문에서는 인간의 시각 정보처리 방법을 기존 문헌을 통해 분석하고 이를 기반으로 한 경계선 추출 모형을 제안한다. 제안된 모형은 기존의 수학적인 시각 모형에서 불가피하게 제외되는 시각 특징 및 인간의 시각기관의 특징을 포함할 수 있을 것으로 기대된다.

• The Korean Journal of Applied Statistics
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• v.33 no.6
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• pp.813-821
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• 2020

Recently, research using unstructured data such as images and videos has been actively conducted in various fields. Edge detection is one of the most useful image enhancement techniques to improve the quality of the image process. However, it is very difficult to perform edge detection in noise images because the edges and noise having high frequency components. This paper uses a linear model and standard deviation as an effective edge detection method for noise images. The edge is detected by the difference between the standard deviation of the pixels included in the pixel block and the standard deviation of the residual obtained by fitting the linear model. The results of edge detection are compared with the results of the Sobel edge detector. In the original image, the Sobel edge detection result and the proposed edge detection result are similar. Proposed method was confirmed that the edge with reduced noise was detected in the various levels of noise images.