• Proceedings of the Korean Information Science Society Conference
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• 2007.06c
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• pp.407-411
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• 2007

이 논문은 우리나라 차량 영상에서 번호판 영역을 추출하는 알고리즘을 제안한다. 우리나라 번호판은 하단에 네개의 숫자를 포함하고 있으므로, 네 개의 숫자를 찾으면 번호판을 추출 할 수 있다. 제안하는 방법은 입력된 영상에서 숫자의 가능성을 가진 연결 요소를 검출하고 이들을 군집화 한다. 군집화 된 연결요소들을 바탕으로 숫자 네개(4-digits) 후보를 생성한다. 4-digits 후보들을 인식하여 숫자의 가능성을 측정하고, 적합도로 변환한다. 후보영역 중 적합도가 가장 높은 영역을 번호판 영역으로 추출한다. 적합도는 Perfect Metrics 방법으로 측정하였다. 제안하는 방법을 주간 영상 4600장과 야간 영상 264장으로 테스트 한 결과 각각 97.23%, 95.45%의 검출률과 0.09%, 0.11%의 오검출률을 얻었다.

• Journal of KIISE:Software and Applications
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• v.31 no.9
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• pp.1161-1170
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• 2004

This paper proposes to segment skin color areas using a clustering algorithm. Most of previously proposed clustering algorithms have some difficulties, since they generally detect hyperspherical clusters, run in a batch mode, and predefine a number of clusters. In this paper, we use a well-known elliptical clustering algorithm, an EM algorithm, and modify it to learn on-line and find automatically the number of clusters, called to an EAM algorithm. The effectiveness of the EAM algorithm is demonstrated on a task of skin color region segmentation. Experimental results present the EAM algorithm automatically finds a right number of clusters in a given image without any information on the number. Comparing with the EM algorithm, we achieved better segmentation results with the EAM algorithm. Successful results were achieved to detect and segment skin color regions using a conditional probability on a region. Also, we applied to classify images with persons and got good classification results.

• Journal of IKEEE
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• v.16 no.2
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• pp.95-101
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• 2012

A novel approach for the reconstruction of 3D building model from aerial image data is proposed in this paper. In this approach, a Centroid Neural Network (CNN) with a metric of line segments is proposed for connecting low-level linear structures. After the straight lines are extracted from an edge image using the CNN, rectangular boundaries are then found by using an edge-based grouping approach. In order to avoid producing unrealistic building models from grouping lined segments, a hierarchical grouping method is proposed in this paper. The proposed hierarchical grouping method is evaluated with a set of aerial image data in the experiment. The results show that the proposed method can be successfully applied for the reconstruction of 3D building model from satellite images.

• Journal of radiological science and technology
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• v.32 no.1
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• pp.45-52
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• 2009

Digital Mammography is an efficient imaging technique for the detection and diagnosis of breast pathological disorders. Six mammographic criteria such as number of cluster, number, size, extent and morphologic shape of microcalcification, and presence of mass, were reviewed and correlation with pathologic diagnosis were evaluated. It is very important to find breast cancer early when treatment can reduce deaths from breast cancer and breast incision. In screening breast cancer, mammography is typically used to view the internal organization. Clusterig microcalcifications on mammography represent an important feature of breast mass, especially that of intraductal carcinoma. Because microcalcification has high correlation with breast cancer, a cluster of a microcalcification can be very helpful for the clinical doctor to predict breast cancer. For this study, three steps of quantitative evaluation are proposed : DoG filter, adaptive thresholding, Expectation maximization. Through the proposed algorithm, each cluster in the distribution of microcalcification was able to measure the number calcification and length of cluster also can be used to automatically diagnose breast cancer as indicators of the primary diagnosis.

• Proceedings of the Korea Information Processing Society Conference
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• 2004.11a
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• pp.839-842
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• 2004

자연영상에서 문자를 인식하는 연구는 활발히 진행되고 있지만 대부분 디지털 카메라나 캠코더 등으로 획득한 고해상도의 영상에서의 연구에 국한되어 있다. 휴대폰 카메라로 획득된 저해상도의 영상은 아주 적은 수의 픽셀로 정보를 표현하기 때문에 기존의 이진화 알고리즘으로는 문자와 배경을 깨끗하게 분리해 낼 수 없다. 본 논문은 영상의 칼라정보를 K-Means 클러스터링을 이용하여 전경과 배경으로 이진화 하였으며, 이진화 성능을 향상시키기 위해 지능형 주파수 필터와 비등방성 확산 필터를 사용하였다. 또한 입력영상을 파이프라인 구조의 이진화 및 인식 시스템에 인식시킴으로써 인식성능을 향상시켰다.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.26 no.5
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• pp.455-463
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• 2008

The shadows, which is generated when acquiring data using optical sensor, mutilates consistency of brightness for same objects in the images. Hence, it makes a trouble to interpret the ground information. This study is focused on detecting the shadowing area in the images. And only single image is used without any other data which is acquired from different source. Also, This study presents the method using HSI color model, especially, using I(intensity) information, and the intensity clustering algorithm. Then, we illuminate the effects of shadow by FFT(Fast Fourier Transform).

• Proceedings of the Korean Information Science Society Conference
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• 2001.10b
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• pp.358-360
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• 2001

본 논문에서는 내용기반 영상검색중 객체기반검색 방법에 대해 다룬다. 먼저 색상과 질감정보가 동일한 영역을 VQ알고리즘을 이용해 군집화 함으로써 동일한 영역을 추출하는 새로운 영상분할기법을 제안하고, 분할 후에 분할에 사용된 색상과 질감정보, 객체간의 위치정보와 영역크기정보를 가지고 객체간 유사도를 판별하여 영상을 검색한다. 이 때 사용되는 색상의 범위의 몇 개의 주요한 색상으로 표시하기 위해 색상테이블을 사용하고 인간의 인지도에 의해 다시 그룹화 함으로써 계산량과 데이터저장의 효율성을 높인다. 영상검색시에는 질의 영상의 관심객체와 비교대상이 되는 데이터베이스 영상의 여러 객체와의 유사성을 판단하여 영상간의 유사도를 계산하는 일대다 매칭 방법(One Object to Multi Objects Matching)과 질의 영상의 여러 객체와 데이터베이스영상의 여러 객체간의 유사도를 판단하는 다대다 매칭 방법(Multi Objects to Multi Objects Matching)을 제안한다. 또한, 제안된 시스템은 고속검색을 실현하기 위해 주요한 색상값을 키(key)색인화 해서 일치가능성이 없는 영상들은 1차적으로 제거함으로써 검색시간을 줄일 수 있도록 했다.

• Journal of Korea Multimedia Society
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• v.6 no.6
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• pp.1000-1007
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• 2003

In this paper, we propose an efficient method for locating lip. The lip shape is represented as a set of points based on Point Distribution Model. We use the Isodata clustering algorithm to find clusters for all training data. For each cluster, a lip shape model is calculated using principle component analysis. For all training data, a lip boundary model is calculated based on the pixel values around the lip boundary. To decide whether a recognition result is correct, we use a cost function based on the lip boundary model. Because of using different models according to the lip shapes, our method can localize correctly the flu far from the mean shape. The experiments have been performed for many images, and show correct recognition rate of 92%.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.10
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• pp.4807-4813
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• 2012

Since the existing methods of segmenting target objects from various images mainly use 2-dimensional features, they have several constraints due to the shortage of 3-dimensional information. In this paper, we therefore propose a new method of accurately segmenting target objects from three dimensional stereoscopic images using 2D and 3D feature clustering. The suggested method first estimates depth features from stereo images by using a stereo matching technique, which represent the distance between a camera and an object from left and right images. It then eliminates background areas and detects foreground areas, namely, target objects by effectively clustering depth and color features. To verify the performance of the proposed method, we have applied our approach to various stereoscopic images and found that it can accurately detect target objects compared to other existing 2-dimensional methods.

• Journal of Digital Convergence
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• v.12 no.11
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• pp.281-288
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• 2014

A method for edge detection using K-means clustering is proposed in this paper. The method is performed through there steps. Histogram equalizing is applied to the image for the uniformed intensity distribution. Pixels are clustered by K-means clustering technique. Then Sobel mask is applied to detect edges. Experiments showed that this method detected edges better than conventional method.