• Proceedings of the Korean Information Science Society Conference
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• 1998.10c
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• pp.532-534
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• 1998

본 논문에서는 칼라공간상의 거리와 이웃정보를 이용한 클러스터링을 통한 칼라영상 분할 방법을 제안한다. 영상의 픽셀들을 이웃관계를 유지하여 칼라공간으로 매핑한다. 칼라공간상에서 이웃하는 픽셀들을 클러스터링하여 영상의 세그먼트들을 찾는다. 클러스터링 방법으로서 인력을 모방하는 클러스터링(gravitational clustering)을 사용하였다. 이 방법으로 클러스터의 중심값과 클러스터 수를 미리 정해주지 않아도 자동적으로 결정할 수 있는 장점이 있다. gravitational 클러스터링에서 찾은 클러스터 수를 가지고 다른 클러스터링 방법에 입력으로 주어 결과를 비교해 본다. 본 논문에서는 이웃관계를 따라 클러스터링하는 것이 정확한 경계선을 찾는데 효과적임을 보여준다.

• Proceedings of the Korean Information Science Society Conference
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• 2003.04a
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• pp.707-709
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• 2003

본 논문에서는 유사도(similarity) 기반 이미지 클러스터링 기법에 대하여 논하고자 한다. 비트맵 이미지의 특징을 추출하고 이러한 특징에 기반한 유사도 측정 함수들을 소개하고 이미지 클러스터링 알고리즘과 구현을 통한 실험 예제들에 대해서 설명한다. 이 실험에서 우리는 유사도에 따라 이미지들이 계층적(Hierarchical)으로 집단화 되는 계층적 클러스터링 알고리즘을 사용하였다. 이미지의 특징 표현을 위해서는 HSV 기반의 히스토그램을 이용하였다. 본 논문에서 제안한 기법의 실험 결과는 이미지 데이터베이스에서 유사한 이미지를 검색하는데 높은 효율성이 있는 것을 보여준다.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.2
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• pp.124-132
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• 2014

In this paper, we present a novel backlit region detection and contrast enhancement method using fuzzy C-means clustering and adaptively partitioned block based contrast stretching. The proposed method separates an image into both dark backlit and bright background regions using adaptively partitioned blocks based on the optimal threshold value computed by fuzzy logic. The detected block-wise backlit region is refined using the guided filter for removing block artifacts. Contrast stretching algorithm is then applied to adaptively enhance the detected backlit region. Experimental results show that the proposed method can successfully detect the backlit region without a complicated segmentation algorithm and enhance the object information in the backlit region.

• Geomechanics and Engineering
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• v.36 no.3
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• pp.205-215
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• 2024

Manually mapping fractures in construction stone mines is challenging, time-consuming, and hazardous. In this method, there is no physical access to all points. In contrast, digital image processing offers a safe, cost-effective, and fast alternative, with the capability to map all joints. In this study, two methods of detecting the trace of discontinuities using image processing in construction stone mines are presented. To achieve this, we employ two modified Hough transform algorithms and the degree of neighborhood technique. Initially, we introduced a method for selecting the best edge detector and smoothing algorithms. Subsequently, the Canny detector and median smoother were identified as the most efficient tools. To trace discontinuities using the mentioned methods, common preprocessing steps were initially applied to the image. Following this, each of the two algorithms followed a distinct approach. The Hough transform algorithm was first applied to the image, and the traces were represented through line drawings. Subsequently, the Hough transform results were refined using fuzzy clustering and reduced clustering algorithms, along with a novel algorithm known as the farthest points' algorithm. Additionally, we developed another algorithm, the degree of neighborhood, tailored for detecting discontinuity traces in construction stones. After completing the common preprocessing steps, the thinning operation was performed on the target image, and the degree of neighborhood for lineament pixels was determined. Subsequently, short lines were removed, and the discontinuities were determined based on the degree of neighborhood. In the final step, we connected lines that were previously separated using the method to be described. The comparison of results demonstrates that image processing is a suitable tool for identifying rock mass discontinuity traces. Finally, a comparison of two images from different construction stone mines presented at the end of this study reveals that in images with fewer traces of discontinuities and a softer texture, both algorithms effectively detect the discontinuity traces.

• Journal of the Korea Institute of Military Science and Technology
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• v.17 no.6
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• pp.810-820
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• 2014

Recently many studies of Radar systems mounted on ground vehicles for autonomous driving, SLAM (Simultaneous localization and mapping) and collision avoidance are reported. In near field, several hits per an object are generated after signal processing of Radar data. Hence, clustering is an essential technique to estimate their shapes and positions precisely. This paper proposes a method of grouping hits in range-doppler domains into clusters which represent each object, according to the pre-defined rules. The rules are based on the perceptual cues to separate hits by object. The morphological connectedness between hits and the characteristics of SNR distribution of hits are adopted as the perceptual cues for clustering. In various simulations for the performance assessment, the proposed method yielded more effective performance than other techniques.

• Journal of Korea Multimedia Society
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• v.20 no.8
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• pp.1224-1235
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• 2017

Recently, augmented reality technology has received much attention in many fields. This paper presents an augmented reality system using Rubiks' Cube which can augment various 3D objects depending on patterns of a Rubiks' cube. The system first detects a cube from an image using partitional clustering and strongly connected graph. Thereafter, the system detects the top side of the cube and finds a proper pattern to determine which object should be augmented. An object corresponding to the pattern is finally augmented according to the camera viewpoint. Experimental results show that the proposed system successfully augments various virtual objects in real time.

• Proceedings of the IEEK Conference
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• 2000.09a
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• pp.363-366
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• 2000

이미지의 한가지 특징(feature)만을 고려한 내용 기반 이미지 검색(content-based image retrieval)은 두가지 이상의 특징 정보를 사용했을 경우와 비교하여 정확도(precision)가 떨어져 성능을 저하시킬 수 있다 따라서 대부분의 검색 시스템에서는 색상(color)이나 형태(shape), 질감(texture) 등과 같은 이미지의 다양한 특징들을 결합하여 검색에 이용하고 있다. 본 논문에서는 이미지의 색상 및 형태 정보를 이용하여 사용자의 질의와 유사한 이미지를 제공하고, 고 차원화된 이미지의 특징들을 클러스터링(clustering) 방법을 이용하여 빠르게 검색할 수 있도록 하였으며, 또한 검색시 그룹 경계 보정 방법을 이용하여 전체 검색을 하지 않고도 전체검색 결과와 동일한 결과를 얻을 수 있는 시스템을 설계 및 구현하였다. 실험에 사용된 데이터는 2022개의 자연 영상이였으며, HSI 색상 정보와 이미지의 에지(edge) 정보를 특징 벡터로 삼았다. 실험 결과, 색상 정보 하나만을 사용한 경우보다 정확도와 재현율면에서 사용자가 원하는 이미지와 보다 유사한 결과를 검출할 수 있었을 뿐만 아니라 클러스터링을 사용함으로써 보다 빠르고, 전체검색 결과와 동일한 검색이 가능하다는 것을 입증하였다.

• International Journal of Contents
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• v.9 no.1
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• pp.6-10
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• 2013

Foreground estimation in object segmentation has been an important issue for last few decades. In this paper we propose a GrabCut based automatic foreground estimation method using block clustering. GrabCut is one of popular algorithms for image segmentation in 2D image. However GrabCut is semi-automatic algorithm. So it requires the user input a rough boundary for foreground and background. Typically, the user draws a rectangle around the object of interest manually. The goal of proposed method is to generate an initial rectangle automatically. In order to create initial rectangle, we use Gabor filter and Saliency map and then we use 4 features (amount of area, variance, amount of class with boundary area, amount of class with saliency map) to categorize foreground and background. From the experimental results, our proposed algorithm can achieve satisfactory accuracy in object segmentation without any prior information by the user.

• Proceedings of the Korean Society of Computer Information Conference
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• 2011.01a
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• pp.249-252
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• 2011

본 논문에서는 쇼크(shock) 그래프 기반의 뼈대 특징을 이용하여 모양 정보를 분류하기 위해 그래프 편집 거리(edit cost) 기반의 k-means 군집화 알고리즘을 적용하는 방법을 제안한다. 본 논문에서 제안된 방법에서는 먼저 질의 영상과 대상 데이터베이스 영상으로부터 뼈대 기반의 쇼크 그래프를 추출한 후 종점(end points)과 분기점(branch points)을 가중치를 이용하여 적응적으로 선택한다. 그런 다음, 두 영상 사이의 편집 거리를 구하여 이를 k-means 군집화 알고리즘의 거리 척도로 적용함으로써 대용량의 영상을 보다 효과적으로 분류한다. 성능을 평가하기 위해서 제안된 알고리즘을 MPEG-7 데이터베이스에 적용하였으며, 그 결과 제안된 영상 분류 방법이 기존의 영상 분류 방법에 비해서 보다 효과적으로 모양 기반의 영상을 분류하였음을 확인하였다.

• Journal of Institute of Control, Robotics and Systems
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• v.3 no.4
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• pp.406-414
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• 1997

A real-time inspection system is developed using line scan cameras. Several improved algorithms are proposed for real-time detection of defects in this automated inspection system. The major improved algorithms include the preprocessing, the threshold decision, and the clustering algorithms. The preprocessing algorithms are for exact binarization and the threshold decision algorithm is for fast detection of defects in 1-D binary images. The clustering algorithm is also developed for fast classifying of the defects. The system is applied to PCBs(Printed Circuit Boards) inspection. The typical defects in PCBs are pits, dent, wrinkle, scratch, and black spots. The results show that most defects are detected and classified successfully.