• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.14 no.9
• /
• pp.4472-4477
• /
• 2013

This paper describes the color features of smoke in each standard color model in order to present the most suitable color model for somke detection in video surveillance system. Histogram intersection technique is used to analyze the difference characteristics between color of smoke and color of non smoke. The considered standard color models are RGB, YCbCr, CIE-Lab, HSV, and if the calculated histogram intersection value is large for the considered color model, then the smoke spilt characteristics are not good in that color model. If the calculated histogram intersection value is small, then the smoke spilt characteristics are good in that color model. The analyzed result shows that the RGB and HSV color models are the most suitable for color model based smoke detection by performing respectively 0.14 and 0.156 for histogram intersection value.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.19 no.5
• /
• pp.697-704
• /
• 2018

Since the face in image content corresponds to individual information that can distinguish a specific person from other people, it is important to accurately detect faces not hidden in an image. In this paper, we propose a method to accurately detect a face from input images using a deep learning algorithm, which is one of the machine learning methods. In the proposed method, image input via the red-green-blue (RGB) color model is first changed to the luminance-chroma: blue-chroma: red-chroma ($YC_bC_r$) color model; then, other regions are removed using the learned skin color model, and only the skin regions are segmented. A CNN model-based deep learning algorithm is then applied to robustly detect only the face region from the input image. Experimental results show that the proposed method more efficiently segments facial regions from input images. The proposed face area-detection method is expected to be useful in practical applications related to multimedia and shape recognition.

• Journal of the Korea Society of Computer and Information
• /
• v.11 no.3
• /
• pp.167-172
• /
• 2006

An image retrieval system retrieves and offers same of similar image based on various features of image. This paper present a brand image retrieval system based on color and shape of image. We use the image for a color information by dividing into the area and extracting the area color distribution histogram. We use for the shape information by preprocessing of the boundary extraction, the centroid extraction, angular sampling etc. and calculating of the sum of the distance from the centroid to the boundary, the standard deviation, and the rate of long axis to short axis. We accomplish the retrieval through a similarity measurement by using the color and shape information which is extracted in this way.

• Proceedings of the Korean Information Science Society Conference
• /
• 2004.10b
• /
• pp.760-762
• /
• 2004

본 논문에서 CT영상에서 간 영역을 자동적으로 분할할 수 있는 방법을 제안한다. 밝기의 특성을 이용하여 초기 관심 영역을 추출하기 위해 ATI(Automatic Threshold Intensity)기법을 사용하였다. 간 영역을 최종적으로 추출하기 위해 블럽 컬러링 기법을 사용하였다 기존 블럽 컬러링의 연산속도를 개선하기 위해서 Recoloring table을 이용하였다 제안된 방법을 이용하여 실험한 결과로 간 영역 추출의 성공률 90%를 얻었다.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.45 no.3
• /
• pp.97-102
• /
• 2008

In this paper we propose an object-based image retrieval method using spatial color model and feature points registration method for an effective color query detection. The proposed method in other to overcome disadvantages of existing color histogram methods and then this method is use the HMMD model and rough set in order to segment and detect the wanted image parts as a real time without the user's manufacturing in the database image and query image. Here, we select candidate regions in the similarity between the query image and database image. And we use SIFT registration methods in the selected region for object retrieving. The experimental results show that the proposed method is more satisfactory detection radio than conventional method.

• Journal of Service Research and Studies
• /
• v.5 no.2
• /
• pp.103-110
• /
• 2015

Recently, Classification of characters in a 3D FPS game has emerged as a very significant issue. In this study, We propose the game character Classification method using Image Region Segmentation of the extracting meaningful object in a simple operation. In this method, first used a non-linear RGB color model and octree color quantization scheme. The input image represented a less than 20 quantized color and uses a small number of meaningful color histogram. And then, the image divided into small blocks, calculate the degree of similarity between the color histogram intersection and adjacent block in block units. Because, except for the block boundary according to the texture and to extract only the boundaries of the object block. Set a region by these boundary blocks as a game object and can be used for FPS game play. Through experiment, we obtain accuracy of more than 80% for Classification method using each feature. Thus, using this property, characters could be classified effectively and it draws the game more speed and strategic actions as a result.

• Journal of IKEEE
• /
• v.13 no.4
• /
• pp.1-7
• /
• 2009

A video segmentation algorithm, which can extract objects even with non-stationary backgrounds, is proposed in this work. The proposed algorithm is composed of three steps. First, we perform an initial segmentation interactively to build the probability density functions of colors per each macro block via kernel density estimation. Then, for each subsequent frame, we construct a coherence strip, which is likely to contain the object contour, by exploiting spatio-temporal correlations. Finally, we perform the segmentation by minimizing an energy function composed of color, coherence, and smoothness terms. Experimental results on various test sequences show that the proposed algorithm provides accurate segmentation results.

• Journal of Korea Multimedia Society
• /
• v.16 no.10
• /
• pp.1135-1146
• /
• 2013

In this paper, we proposed a new algorithm for individual tooth region segmentation on tooth color images. The proposed algorithm used oral cavity model based on structural feature of tooth and new boundary of watershed algorithm. First, the gray scale image is obtained with emphasized tooth regions from the color images and unnecessary regions are removed on tooth images. Next, the image enhancement of tooth images is implemented using the proposed oral cavity model, and the individual tooth regions are segmented by watershed algorithm on the enhanced images. Boundary and seeds necessary to watershed algorithm are applied boundary of binary image using minimum thresholding and region maximum value. In order to evaluate performance of proposed algorithm, we conduct experiment to compare conventional algorithm with proposed algorithm. As a result of experiment, we confirmed that the proposed algorithm is more improved detection ratio than conventional algorithm at molar regions and the tooth region detection performance is improved by preventing overlap detection on oral cavity.

• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.48 no.5
• /
• pp.37-44
• /
• 2011

This paper proposes a confusion-line separating algorithm in a CIE Lab color space using color segmentation for protanopia and deuteranopia. Images are segmented into regions by grouping adjacent pixels with similar color information using the hue components of the images. To this end, the region growing method and the seed points used in this method are the pixels that correspond to peak points in hue histograms that went through a low pass filter. In order to establish a color vision deficiency (CVD) confusion line map, we established 512 virtual boxes in an RGB 3-D space so that boxes existing on the same confusion line can be easily identified. After that, we checked if segmented regions existed on the same confusion line and then performed color adjustment in an CIE Lab color space so that all adjacent regions exist on different confusion lines in order to provide the best color identification effect to people with CVDs.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.18 no.2
• /
• pp.216-222
• /
• 2008

This paper proposed the multi characters detection algorithm using Color segmentation and the closing curve feature of LoG Operator in order to complement the demerit of the existing research which is weak in complexity of background, variety of light and disordered line and similarity of left and background color, etc. The proposed multi characters detection algorithm divided into three parts : The feature detection, characters format and characters detection Parts in order to be possible to apply to image of various feature. After preprocess that the new multi characters detection algorithm that proposed in this paper used wavelet, morphology, hough transform which is the synthesis logical model in order to raise detection rate by acquiring the non-perfection characters as well as the perfection characters with processing OR operation after processing each color area by AND operation sequentially. And the proposal algorithm is simulated with natural images which include natural character area regardless of size, resolution and slant and so on of image. And the proposal algorithm in this paper is confirmed to an excellent detection rate by compared with the conventional detection algorithm in same image.