• Proceedings of the Korea Contents Association Conference
• /
• 2006.05a
• /
• pp.401-404
• /
• 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 proposed method use by merging constraints to decide whether regions is 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.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.45 no.4
• /
• pp.34-40
• /
• 2008

This paper presents a vision based walking robot control system using hand shape recognition. To recognize hand shapes, the accurate hand boundary needs to be tracked in image obtained from moving camera. For this, we use an active contour model-based tracking approach with mean shift which reduces dependency of the active contour model to location of initial curve. The proposed system is composed of four modules: a hand detector, a hand tracker, a hand shape recognizer and a robot controller. The hand detector detects a skin color region, which has a specific shape, as hand in an image. Then, the hand tracking is performed using an active contour model with mean shift. Thereafter the hand shape recognition is performed using Hue moments. To assess the validity of the proposed system we tested the proposed system to a walking robot, RCB-1. The experimental results show the effectiveness of the proposed system.

• 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%.

• Journal of Advanced Navigation Technology
• /
• v.23 no.1
• /
• pp.77-83
• /
• 2019

In this paper, we propose an object tracking algorithm based on color histogram and convolutional neural network model. In order to increase the tracking accuracy, we synthesize generic object tracking using regression network algorithm which is one of the convolutional neural network model-based tracking algorithms and a mean-shift tracking algorithm which is a color histogram-based algorithm. Both algorithms are classified through support vector machine and designed to select an algorithm with higher tracking accuracy. The mean-shift tracking algorithm tends to move the bounding box to a large range when the object tracking fails, thus we improve the accuracy by limiting the movement distance of the bounding box. Also, we improve the performance by initializing the tracking start positions of the two algorithms based on the average brightness and the histogram similarity. As a result, the overall accuracy of the proposed algorithm is 1.6% better than the existing generic object tracking using regression network algorithm.

• Journal of Korean Academy of Nursing
• /
• v.24 no.2
• /
• pp.175-189
• /
• 1994

This study examined the daily rhythmic patterns of mood in shift workers. Ten rotating shift nurses (shift worker group) were matched with ten non-rotating student nurses (non - shift worker group) working under the same conditions at University Hospital. The subjects completed the Mood Adjective Checkist (MAC) every two or three hours from 6AM to 9-11 PM for six consecutive days. The MAC was constructed by Mansour and conversed the mood factors of Anger - Depression. Happiness, Mental, and Social. These data were analyzed by using Cosinor method. The results are summarized as follows : 1. There was no difference in mean scores for Anger - Depression, Happiness, Mental, and Social mood rhythm between the shift workers and the non - shift workers. 2. There was no difference in the amplitude of Anger - Depression, Happiness and Social mood between the two groups, but the shift workers had a higher amplitude of Mental mood. 3. The acrophases of the Anger - Depression mood were between 1:28 and 2:05, and those of Happiness, Social, and Mental mood were between 12:5 and 15:03 for both groups. There were no differences between the groups. 4. The number of the subjects with statistically significant mean cosinor rhythms for Anger-De-pression and Mental moods were higher in the shift workers than in the non-shift workers, but there were no differences between the shift workers and the non-shift workers in those of Happiness and Mental mood. This study showed that the mod manifested circadian periodicities, and a rapidly rotating shift system did not changed the circadian rhythm of mood. It is expected that this study will facilitate a better understanding of circadian rhythm in mood in the shiftworkers.

• Journal of Korean Biological Nursing Science
• /
• v.19 no.3
• /
• pp.198-205
• /
• 2017

Purpose: The purpose of this study was to identify sleep patterns, alertness, and fatigue of shift nurses according to circadian types. Methods: The researchers' enrolled 17 nurses doing shift work in a tertiary hospital. To evaluate circadian types, a morningness-eveningness questionnaire (MEQ) was administered. Sleep patterns were examined using an actigraph for 14 days. To assess alertness and fatigue, Visual Analogue Scale (VAS) was used. The data were analyzed using ANOVA and Kruskal-Wallis test with a SPSS 21.0 program. Results: The researchers found that 17.6% of participants reported morning type, 47.1% neither type, and 35.3% evening type. Mean total sleep time (TST) was 6.8 h, mean sleep efficacy was 82%, level of alertness was 6.54, and level of fatigue was 5.49, regardless of the type of shift work. Evening type nurses had higher variation in TST and alertness, according to the shift patterns than other circadian type nurses. Evening type nurses also had higher fatigue levels than other circadian type nurses. Conclusion: Sleep, alertness, and fatigue were related with circadian types. These results suggest that circadian rhythm management in shift work nurses, particularly in evening type nurses is urgently needed to improve sleep patterns, alertness, and to decrease the level of fatigue.

• Journal of Korean Institute of Industrial Engineers
• /
• v.20 no.1
• /
• pp.3-13
• /
• 1994

The problem of selecting the optimal target values in a canning process is considered for situations where there is a linear shift in the mean of the content of a can which is assumed to be normally distributed with known variance. The target values are initial process mean, length of resetting cycle and controllable upper limit. Profit models are constructed which involve give-away, rework, and resetting costs. Methods of finding the optimal target values are presented and a nemerical example is given.

• Journal of Korean Society for Quality Management
• /
• v.33 no.3
• /
• pp.91-104
• /
• 2005

Tolerance analysis plays an important role in design and manufacturing stages for reducing manufacturing cost by improving producibility. In most production processes encountered in practice, a process mean may shift or drift in the long run although process is in control. This study discusses the feasibility of three most common inflation factors(Bender, Gilson and Six Sigma) as a correction to Root Sum of Squares(RSS) method to compensate heuristically for a shift of process mean and nonnormal component distributions from simulation experiments and proposes the guidelines for choosing the inflation factor.

• Proceedings of the KIEE Conference
• /
• 2006.07d
• /
• pp.1973-1974
• /
• 2006

The mean shift algorithm has achieved considerable success in object tracking due to its simplicity and robustness. It finds local maxima of a similarity measure between the color histograms of the target and candidate image. However, the mean shift tracking algorithm using only color histograms has a serious defect. It doesn't use the spatial information of the target. Thus, it is difficult to model the target more exactly. And it is likely to lose the target during the occlusions of other objects which have similar color distributions. To deal with these difficulties we use both color information and spatial information of the target. Our proposed algorithm is robust to occlusions and scale changes in front of dynamic, unstructured background. In addition, our proposed method is computationally efficient. Therefore, it can be executed in real-time.

• Proceedings of the KIEE Conference
• /
• 2006.07d
• /
• pp.1933-1934
• /
• 2006

이 논문은 비강체 객체에 대한 실시간 추적시 다른 객체에 의한 간섭의 영향을 줄이는 방법을 제시한다. 제시한 방법에서 객체 추적에 대한 알고리즘은 현재 프레임에서 확률적으로 목표의 위치를 찾는 Mean shift 방법에 기초를 두고 있다. 기존의 방법에서는 mean shift 의 파라미터로서 색분포만 사용한다. 하나의 파라미터에만 의존하므로 같은 색분포를 갖는 다른 객체가 추적 영역 내에 들어오게 되면 새로운 객체를 기존 객체로 인식하게 되는 문제가 발생한다. 여기서 우리는 강인한 객체추적을 하기 위해 다른 하나의 파라미터로서 거리정보를 이용을 제안한다. 거리정보에 최적화된 흐름 추정(optical flow estimation)방법을 확장 도입한 강인한 에러 기준(robust error norm) 방법을 사용하여 기존의 객체에게 더 많은 가중치를 주는 방식으로 mean shift 추적 방법을 기초로 하여 강력하게 추적하는 방법을 제안한다.