• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.4
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• pp.467-472
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• 2005

The principal objective of enhancement methods is to process an image so that the output image is more suitable than the original image lot a specific application. Images taken in the night can be low-contrast images because of poor environments. In this paper, we compared the performance of Image Contrast Enhancement Technique Using Clustering Algorithm(ICECA) with those of color adjustment methods such as Histogram Equalization(HE), Brightness Preserving Bi-Histogram Equalization(BBHE), and the Multi-Scale Refiner(MSR). We compared these methods by applying the image enhancement methods to a set of diverse images.

• Research in Community and Public Health Nursing
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• v.23 no.4
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• pp.405-414
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• 2012

Purpose: The purpose of this study was to examine the sexual maturation, body image, and self-esteem of Korean elementary school girls with symptoms of precocious puberty compared to those with no symptoms of precocious puberty. Methods: The subjects were 309 girls of lower grades in elementary school. Tanner's Sexual Maturation Rating (SMR), Self Image Scale, and Self-esteem Scale were utilized to determine the presence of symptoms of precocious puberty, body image, and self esteem. Collected data were analyzed by Chi-square test, independent t-test, and one-way ANOVA using the SPSS/WIN 17.0 program. Results: The percentage of the girls with breast development was 14.9%. Breast development usually began in the third grade (56.5%). Of the subjects, 0.3% were experiencing menstruation. Breast development was related to grade, age, height, weight, and a cause of worry. There was a significant difference of body image between girls with breast development in the first grade and in the second grade. Conclusion: An effective intervention that can improve the self-image of children with symptoms of precocious puberty should be developed to prevent and treat physical and mental problems related to sexual maturation.

• The Journal of Korean Obstetrics and Gynecology
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• v.21 no.1
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• pp.179-198
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• 2008

Purpose: Obesity is already a major clinical problem in Korea, so obesity prevention and treatment practices are gradually interested in Korean Medicine doctors. Moreover subjective body shape image entered as a important medical topic recently. To make a graph system for clinical tool which based on the Korean national standard physical scale. Methods: Physical data of the girth of upper arm, abdomen, hip, buttock, flank, calf, and body weight were statistically analyzed, which collected through the fifth national standard physical scale research. And then reviewed those statistical validation. Results: Relationship of the each body scale data and those graphs were fit and right statistically. Conclusion: These new achieved graph system of Korean standard body scales could be adopt as a useful clinical tool for body image related patients such as obesity, PCOD and postpartum patient.

• Journal of Korea Multimedia Society
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• v.19 no.12
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• pp.1951-1959
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• 2016

In this paper, we propose a two-scale fusion method for infrared and visible images using saliency and variance. The images are separated into two scales respectively: a base layer of low frequency component and a detailed layer of high frequency component. Then, these are synthesized using weight. The saliencies and the variances of the images are used as the fusion weights for the two-scale images. The proposed method is tested on several image pairs, and its performance is evaluated quantitatively by using objective fusion metrics.

• Proceedings of the KSME Conference
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• 2008.11a
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• pp.1594-1599
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• 2008

Haptic rendering is a process that provides force feedback during interactions between a user and an object. This paper presents a haptic rendering technique for a telemanipulation system of deformable objects using image processing and physically based modeling techniques. The interaction forces between an instrument driven by a haptic device and a deformable object are inferred in real time based on a continuum mechanics model of the object, which consists of a boundary element model and ${\alpha}$ priori knowledge of the object's mechanical properties. Macro- and micro-scale experimental systems, equipped with a telemanipulation system and a commercial haptic display, were developed and tested using silicone (macro-scale) and zebrafish embryos (micro-scale). The experimental results showed the effectiveness of the algorithm in different scales: two experimental systems applied the same algorithm provided haptic feedback regardless of the system scale.

• Journal of Advanced Marine Engineering and Technology
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• v.29 no.2
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• pp.217-224
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• 2005

A new roof edge detection method based on multi level scales of wavelet function is proposed in this paper roof edge and its direction are obtained in this new methods at one time. Besides. scale characteristics of detecting roof edge is analyzed. And a few new methods on fingerprint image pre-processing are described. A method segmenting foreground/background of fingerprint images is proposed, in which Prior estimation of direction field is not required any more. A segmentation method based on multi-scale roof edges is implemented. and the valid scale range of the method is defined. too. And the method is used to segment ridges and valleys in fingerprint images simultaneously The exact direction fields made up of the direction of each point in ridges can be obtained when detecting ridges exactly based on the roof edge detector, in comparison with the traditional coarse estimation of direction fields. Obviously. it will establish a solid foundation for the sequent fingerprint identification.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.8
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• pp.88-100
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• 1999

Detecting human face and facial landmarks automatically in an image is as essential step to a fully automatic face recognition system. In this paper, we present a new approach to detect automatically face and its eyes of input image with scale and rotation variations of faces by using an intensity based template matching with a single log-polar face template. In a template-based matching it is necessary to normalize the scale changes and rotations of an input image to a template ones. The log-polar mapping which simulates space-variant human visual system converts scale changes and rotations of input image into constant horizontal and cyclic vertical shifts in the output plane. Intelligent use of this property allows us to shift of the candidate log-polar faces mapped at various fixation points of an input image to be matched to a template over the log-polar plane. Thus, the proposed method eliminates the need of adapting multitemplate and multiresolution schemes, which inevitably give rise to intensive computation involved to cope with scale and rotation variations of faces. Through this scale and rotation involved to cope with scale and method can lead to detecting face and its eyes simultaneously. Experimental results on a database of 795 images show over 98% detection rate.

• Journal of Biomedical Engineering Research
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• v.21 no.4
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• pp.333-338
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• 2000

In this paper, an automated segmentation algorithm is proposed for MR brain images using T1-weighted, T2-weighted, and PD images complementarily. The proposed segmentation algorithm is composed of 3 step. In the first step, cerebrum images are extracted by putting a cerebrum mask upon the three input images. In the second step, outstanding clusters that represent inner tissues of the cerebrum are chosen among 3-dimensional(3D) clusters. 3D clusters are determined by intersecting densely distributed parts of 2D histogram in the 3D space formed with three optimal scale images. Optimal scale image is made up of applying scale space filtering to each 2D histogram and searching graph structure. Optimal scale image best describes the shape of densely distributed parts of pixels in 2D histogram and searching graph structure. Optimal scale image best describes the shape of densely distributed parts of pixels in 2D histogram. In the final step, cerebrum images are segmented using FCM algorithm with its initial centroid value as the outstanding clusters centroid value. The proposed cluster's centroid accurately. And also can get better segmentation results from the proposed segmentation algorithm with multi spectral analysis than the method of single spectral analysis.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.1243-1244
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• 2022

In recent years, the growing interest in off-site construction has led to factories scaling up their manufacturing and production processes in the construction sector. Consequently, continuous large-scale site monitoring in low-variability environments, such as prefabricated components production plants (precast concrete production), has gained increasing importance. Although many studies on computer vision-based site monitoring have been conducted, challenges for deploying this technology for large-scale field applications still remain. One of the issues is collecting and transmitting vast amounts of video data. Continuous site monitoring systems are based on real-time video data collection and analysis, which requires excessive computational resources and network traffic. In addition, it is difficult to integrate various object information with different sizes and scales into a single scene. Various sizes and types of objects (e.g., workers, heavy equipment, and materials) exist in a plant production environment, and these objects should be detected simultaneously for effective site monitoring. However, with the existing object detection algorithms, it is difficult to simultaneously detect objects with significant differences in size because collecting and training massive amounts of object image data with various scales is necessary. This study thus developed a large-scale site monitoring system using edge computing and a small-object detection system to solve these problems. Edge computing is a distributed information technology architecture wherein the image or video data is processed near the originating source, not on a centralized server or cloud. By inferring information from the AI computing module equipped with CCTVs and communicating only the processed information with the server, it is possible to reduce excessive network traffic. Small-object detection is an innovative method to detect different-sized objects by cropping the raw image and setting the appropriate number of rows and columns for image splitting based on the target object size. This enables the detection of small objects from cropped and magnified images. The detected small objects can then be expressed in the original image. In the inference process, this study used the YOLO-v5 algorithm, known for its fast processing speed and widely used for real-time object detection. This method could effectively detect large and even small objects that were difficult to detect with the existing object detection algorithms. When the large-scale site monitoring system was tested, it performed well in detecting small objects, such as workers in a large-scale view of construction sites, which were inaccurately detected by the existing algorithms. Our next goal is to incorporate various safety monitoring and risk analysis algorithms into this system, such as collision risk estimation, based on the time-to-collision concept, enabling the optimization of safety routes by accumulating workers' paths and inferring the risky areas based on workers' trajectory patterns. Through such developments, this continuous large-scale site monitoring system can guide a construction plant's safety management system more effectively.

• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.670-673
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• 2002

This paper proposes a digital algorithm for gray-scale/color image segmentation of real-time video signals and a cell-network-based implementation architecture in state-of-the-art CMOS technology. Through extrapolation of design and simulation results we predict that about 300$\times$300 pixels can be integrated on a chip at 100nm CMOS technology, realizing very high-speed segmentation at about 1600sec per color image. Consequently real-time color-video segmentation will become possible in near future.