• Proceedings of the Korean Society of Computer Information Conference
• /
• 2010.07a
• /
• pp.433-434
• /
• 2010

본 논문에서는 영상처리에서 많이 사용하는 bilateral filtering과 mean shift segmentation을 이용하여 일반적인 사진을 수채화 스타일로 변환하는 기법에 대하여 제안한다. 먼저 bilateral filtering을 이용하여 사진의 외곽선 부분은 보존하면서 고주파 성분을 약화시키도록 한다. 그리고 bilateral filtering된 영상에서 mean shift segmentation을 수행하여 수채화 스타일의 영상을 생성한다. 본 논문에서 제안하는 기법으로 다양한 사진에 대하여 실험한 결과 수채화 스타일로 잘 변화되는 것을 확인하였으며 특히 주광에서 촬영한 풍경 사진들에 대하여 보다 우수한 성능을 보임을 확인하였다.

• Journal of KIISE:Software and Applications
• /
• v.36 no.11
• /
• pp.936-946
• /
• 2009

A graph cuts method has recently attracted a lot of attentions for image segmentation, as it can globally minimize energy functions composed of data term that reflects how each pixel fits into prior information for each class and smoothness term that penalizes discontinuities between neighboring pixels. In previous approaches to graph cuts-based automatic image segmentation, GMM(Gaussian mixture models) is generally used, and means and covariance matrixes calculated by EM algorithm were used as prior information for each cluster. However, it is practicable only for clusters with a hyper-spherical or hyper-ellipsoidal shape, as the cluster was represented based on the covariance matrix centered on the mean. For arbitrary-shaped clusters, this paper proposes graph cuts-based image segmentation using mean shift analysis. As a prior information to estimate the data term, we use the set of mean trajectories toward each mode from initial means randomly selected in $L^*u^*{\upsilon}^*$ color space. Since the mean shift procedure requires many computational times, we transform features in continuous feature space into 3D discrete grid, and use 3D kernel based on the first moment in the grid, which are needed to move the means to modes. In the experiments, we investigate the problems of mean shift-based and normalized cuts-based image segmentation methods that are recently popular methods, and the proposed method showed better performance than previous two methods and graph cuts-based automatic image segmentation using GMM on Berkeley segmentation dataset.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2009.05a
• /
• pp.407-410
• /
• 2009

본 논문에서는 개선된 mean shift를 이용한 컬러 영상 분할을 소개한다. Mean shift는 Yizong Cheng에 의해 재조명되고 Dorin Comaniciu 등에 의해 정리되어 영상 필터링(image filtering), 영상 분할(image segmentation), 물체 추적(object tracking) 등 여러 응용 분야에 널리 활용되고 있다. 커널을 이용해 밀도를 추정하고 밀도가 가장 높은 점으로 커널을 연속적으로 이동함으로써 지역적으로 주요한 위치로 데이터 값을 갱신시킨다. 그러나 영상에 포함된 모든 화소에 대해 mean shift를 수행해야하기 때문에 연산 시간이 많이 소요되는 단점이 있다. 본 논문에서는 mean shift 필터링 과정을 분석하고 참조수렴방법과 강제수렴방법을 이용해 소요 시간을 단축시켰다. 모든 점에 대해 mean shift를 수행하는 대신 특정 조건을 만족하는 픽셀은 이웃 픽셀의 수렴 값을 참조하고, mean shift 과정에 진동 또는 미미한 이동을 계속하는 픽셀은 강제 수렴을 실시하였다. 개선된 방법과 기존의 mean shift 방식을 적용하여 영상 필터링과 영상 분할에 적용한 실험에서 결과 영상에는 차이가 적고 기존의 방법에 비해 수행 시간이 24% 정도 소요됨을 확인하였다.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.14 no.11
• /
• pp.2413-2418
• /
• 2010

We propose a retouching method that converts a photography taken by smartphone to a watercolor painting image using bilateral filtering and mean shift segmentation which are mostly used in image processing. The first step is to convert an input image to fit the screen resolution of smartphone. And next step is to weaken high frequency components of the image, while preserving the edge of image using the bilateral filtering. And after that we perform mean shift segmentation from the bilateral filtered image. We apply parameters of mean shift segmentation considering the processing speed of smartphone. Experimental result shows that our method can be applied to various types of image and bring better result.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.9 no.6
• /
• pp.163-168
• /
• 2009

In this paper, We proposed the efficient method of cleaning impulse noise using mean shift segmentation. This method do its job for the pixel which is identified as impulse noise using mean shift segmentation instead of all pixel of image by the existing method. we found that the quality of image is improved by measuring the sum of square error in result image and impulse noise is cleaned efficiently by doing experiment.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2010.10a
• /
• pp.206-208
• /
• 2010

We propose a retouching method that converts a photography taken by smartphone to a watercolor painting image using bilateral filtering and mean shift segmentation which are mostly used in image processing. The first step is to convert an input image to fit the screen resolution of smartphone. And next step is to weaken high frequency components of the image, while preserving the edge of image using the bilateral filtering. And after that we perform mean shift segmentation from the bilateral filtered image. We apply parameters of mean shift segmentation considering the processing speed of smartphone. Experimental result shows that our method can be applied to various types of image and bring better result.

• Journal of the Korea Society of Computer and Information
• /
• v.15 no.9
• /
• pp.25-33
• /
• 2010

We propose a retouching method that converts a general photography to a watercolor painting image using bilateral filtering and mean shift segmentation which are mostly used in image processing. The first step is to weaken high frequency components of the image, while preserving the edge of image using the bilateral filtering. And after that we perform DoG(Difference of Gradient) edge extraction and mean shift segmentation respectively from the bilateral filtered image. The DoG edge extraction is performed using luminance component of the image whose RGB color space is transformed into CIELAB space. Experimental result shows that our method can be applied to various types of image and bring better result, especially against the photo taken in daylight.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2022.01a
• /
• pp.41-44
• /
• 2022

Mean-Shift 영상 분할은 객체 검출을 위한 영상 전처리 방법으로써, 영상 처리 및 패턴 인식 분야에서 널리 사용되는 방법이다. 영상 분할은 영역 기반과 에지 기반 방식으로 나누어지며 대표적으로 FCM, Quickshift, Felzenszwalb, SLIC 알고리즘 등 이 있다. 언급한 영상 분할 방법들은 Mean-Shift 영상 분할에 비해서 빠른 속도로 실행시킬 수 있지만, 형태적 특징이 훼손되고 하나의 객체가 여러 세그멘테이션으로 분할된다는 단점을 가지고 있다. 본 논문에서는 소형 객체를 탐지하기 위한 고속화된 Mean-Shift 영상 분할과 객체의 형태적 특징을 이용하여 객체를 탐지하는 방법을 제안한다. 하드웨어 리소스가 제한된 신호처리기에 제안하는 알고리즘을 수행하기 위하여 Mean-Shift 영상 분할에서 필터링 과정을 고속화 하였고, 적외선 영상 내 영상 전처리 수행을 통해 잡음 제거 후 Mean-Shift 영상 분할 방법을 수행함으로써, 객체의 형태적 특징을 잘 살려서 영상 분할을 할 수 있도록 하였다. 또한 각 세그멘테이션의 크기, 너비, 높이, 밝기 정보와 형태적 특징점을 이용한 객체 탐지 방법을 제안한다.

• The KIPS Transactions:PartB
• /
• v.17B no.3
• /
• pp.239-248
• /
• 2010

Leukocyte in blood smear image provides significant information to doctors for diagnosis of patient health status. Therefore, it is necessary step to separate leukocyte from blood smear image among various blood cells for early disease prediction. In this paper, we present a saliency map and stepwise region merging based leukocyte segmentation method. Since leukocyte region has salient color and texture, we create a saliency map using these feature map. Saliency map is used for sub-image separation. Then, clustering is performed on each sub-image using mean-shift. After mean-shift is applied, stepwise region-merging is applied to particle clusters to obtain final leukocyte nucleus. The experimental results show that our system can indeed improve segmentation performance compared to previous researches with average accuracy rate of 71%.

• The Journal of the Korea Contents Association
• /
• v.6 no.9
• /
• pp.19-27
• /
• 2006

Mean shift procedure is applied for the data points in the joint spatial-range domain and achieves a high quality. However, a color image is segmented differently according to the inputted spatial parameter or range parameter and the demerit is that the image is broken into many small regions in case of the small parameter. In this paper, to improve this demerit, we propose the method that groups similar regions using region merging method for over-segmented images. The proposed method converts a over-segmented image in RGB color space into in HSI color space and merges similar regions by hue information. Here, to preserve edge information, the region merge constraints are used to decide whether regions are merged or not. After then, we merge the regions in RGB color space for non-processed regions in HSI color space. Experimental results show the superiority in region's segmentation results.