• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.7 no.6
• /
• pp.1100-1105
• /
• 2006

Severe distortions of colors in the obtained images have made it difficult for color codes to expand their applications. To reduce the effect of color distortions on reading colors, it will be more desirable to statistically process as many pixels in the individual color region as possible, than relying on some regularly sampled pixels. This process may require segmentation, which usually requires edge detection. However, edges in color codes can be disconnected due tovarious distortions such as zipper effect and reflection, to name a few, making segmentation incomplete. Edge linking is also a difficult process. In this paper, a more efficient approach to reducing the effect of color distortions on reading colors, one that excludes precise edge detection for segmentation, was obtained by employing the k-means clustering algorithm. And, in detecting color codes, the properties of both six safe colors and grays were utilized. Experiments were conducted on 144, 4M-pixel, outdoor images. The proposed method resulted in a color-code detection rate of 100% fur the test images, and an average color-reading accuracy of over 99% for the detected codes, while the highest accuracy that could be achieved with an approach employing Canny edge detection was 91.28%.

• The Transactions of the Korea Information Processing Society
• /
• v.4 no.1
• /
• pp.270-279
• /
• 1997

The conversion of printed maps into computerized databases is an enormous task. Thus the automation of the conversion process is essential. Efficient computer representation of printed maps and line drawings depends on codes assigned to characters, symbols, and vector representation of the graphics. In many cases, maps are constructed in a number of layers, where each layer is printed in a distinct color, and it represents a subset of the map information. In order to properly represent the character layer from color map images, an interactive clustering and character extraction technique is proposed. Character is usually separated from graphics by extracting and classifying connected components in the image. But this procedure fails, when characters touch or overlap lines-something that occurs often in land register maps. By vectorizing line segments, the touched characters and numbers are extracted. The algorithm proposed in this paper is intended to contribute towards the solution of the color image clustering and touched character problem.

• Proceedings of the KAIS Fall Conference
• /
• 2007.11a
• /
• pp.209-210
• /
• 2007

불변 특징 기반의 파노라마 생성 방법은 직접 방법에 비해 비교적 처리 속도가 빠르다. 파노라마 생성 과정에서 특징점 추출과 특징 정합에 대부분의 시간이 소요된다. 본 논문에서는 파노라마 생성을 위한 특징점 클러스터링 방법을 제안한다. LoG 영상에서 특징점들을 추출한 후, 클러스터링을 통해 특징점들을 군집화한다. 군집도가 강한 특징점들은 그렇지 않은 특징점들보다 더 의미 있으므로, 파노라마 생성에서 군집도가 약한 군집을 배제함으로써 정확도가 높아지고 처리 시간이 빨라지는 장점이 있다. 실험에서 $320{\times}240$ 크기의 칼라 영상에 대해 제안한 방법의 처리 시간이 약2.0초로 클러스터링 처리를 하지 않는 방법에 비해 약 2배 빠른 결과를 보였다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2012.05a
• /
• pp.649-651
• /
• 2012

In this paper, a method of segmenting color image using characteristics of superpixels is proposed. A superpixel is consist of several pixels with same features such as luminance, color, textures etc. The superpixel can be used for image processing and analysis with large scale image to get high speed processing. A color image can be transformed to $La^*b^*$ feature space having good characteristics, and the superpixels are grouped by clustering and gradient-based algorithm.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2009.05a
• /
• pp.868-871
• /
• 2009

We propose an global color model based method for tracking motions of multiple human using a networked multiple-camera system in intelligent space as a human-robot coexistent system. An intelligent space is a space where many intelligent devices, such as computers and sensors(color CCD cameras for example), are distributed. Human beings can be a part of intelligent space as well. One of the main goals of intelligent space is to assist humans and to do different services for them. In order to be capable of doing that, intelligent space must be able to do different human related tasks. One of them is to identify and track multiple objects seamlessly.

• Journal of Korea Multimedia Society
• /
• v.15 no.6
• /
• pp.761-769
• /
• 2012

In this paper, a setting method of initial cluster centers and color image segmentation using superpixels and Fuzzy C-means(FCM) algorithm is proposed. Generally, the FCM can be widely used to segment color images, and an element is assigned to any cluster with each membership values in the FCM. However the algorithm has a problem of local convergence by determining the initial cluster centers. So the selection of initial cluster centers is very important, we proposed an effective method to determine the initial cluster centers using superpixels. The superpixels can be obtained by grouping of some pixels having similar characteristics from original image, and it is projected $La^*b^*$ feature space to obtain the initial cluster centers. The proposed method can be speeded up because number of superpixels are extremely smaller than pixels of original image. To evaluate the proposed method, several color images are used for computer simulation, and we know that the proposed method is superior to the conventional algorithm by the experimental results.

• Proceedings of the KSRS Conference
• /
• 2008.03a
• /
• pp.20-24
• /
• 2008

차량에 부착된 CCD 카메라를 이용하여 취득된 도로 주변의 영상에 존재하는 사람의 얼굴을 추출하여 제거하는 처리를 할 경우, 사생활 침해의 문제 없이 사용자들에게 원하는 지역의 도로영상의 제공이 가능해진다. 이 실험의 목적은 차량에서 취득된 도로 주변의 칼라 영상에서 사람의 얼굴을 자동으로 추출하는 기술을 개발하는데에 있다. 도로 주변의 CCD영상에서의 얼굴 추출을 위해, HSI(색상, 채도, 명도) 칼라 모델과 YCrCb 칼라 모델을 사용하여 이들 모델에 임계값을 적용하여 피부색을 검출하였으며, 두 개의 모델을 사용한 결과 효과적인 피부색의 검출이 가능함을 확인할 수 있었다. 검출된 피부색 영역을 연결성과 밝기 차이를 이용하여 클러스터링을 실행하고 이렇게 나뉘어진 각각의 구역들에 구역의 면적, 구역내 존재하는 화소의 개수, 구역의 가로와 세로 비율 그리고 타원조건을 적용하여 얼굴 후보 구역을 결정하였다. 그리고 최종적으로 남겨진 구역을 이진화 하고, 이진화 된 영상 중 검은 부분이 5% 이상일 때 이들을 눈, 코, 입 등으로 간주하여 최종적인 얼굴로 결정하였다. 실험 결과 추출되지 않은 얼굴과 잘못 추출된 구역이 발생했으나, 얼굴에 해당하는 임계값등의 조건을 약화시킬 경우 대부분의 얼굴의 추출이 가능할 것으로 여겨지며, 추출된 구역을 흐리게 처리할 경우 오인식된 부분에 대한 사용자의 거부감도 줄일 수 있을 것 으로 예상된다.

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

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.24 no.9B
• /
• pp.1731-1741
• /
• 1999

Color images are often corrupted by the noise due to noisy sensors or channel transmission errors. Some filters such as vector media and vector $\alpha$-trimmed mean filter have bee used for color noise removal. In this paper, We propose the fuzzy cluster filters based on the possibilistic c-means clustering, because the possibilistic c-means clustering can get robust memberships in noisy environments. Also, we propose weighted vector $\alpha$-trimmed mean filter to improve the conventional vector $\alpha$-trimmed mean filter. In this filter, the central data are more weighted than the outlying data. In this paper, we implemented the color noise generator to evaluate the performance of the proposed filters in the color noise environments. The NCD measure and visual measure by human observer are used for evaluation the performance of the proposed filters. In the experiment, proposed fuzzy cluster filters in the sense of NCD measure gave the best performance over conventional filters in the mixed noise. Simulation results showed that proposed weighted vector $\alpha$-trimmed mean filters better than the conventional vector $\alpha$-trimmed mean filter in any kinds of noise.

• Proceedings of the IEEK Conference
• /
• 2003.11a
• /
• pp.377-380
• /
• 2003

A new color image segmentation scheme is presented in this paper. The proposed algorithm consists of image simplification, region labeling and color clustering. The vector-valued diffusion process is performed in the perceptually uniform LUV color space. We present a discrete 3-D diffusion model for easy implementation. The statistical characteristics of each labeled region are employed to estimate the number of total clusters and agglomerative hierarchical clustering is performed with the estimated number of clusters. Since the proposed clustering algorithm counts each region as a unit, it does not generate oversegmentation along region boundaries.