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

• Journal of the Korean Home Economics Association
• /
• v.46 no.6
• /
• pp.49-59
• /
• 2008

The purpose of this study was to examine the role of consumers' self-image and pursued-image of clothes on the clothing purchase decision making according to the location. Data were obtained from a questionnaire filled out by 575 women living in Seoul and Jechon. For data comparative analysis, paired t-test, t-test, factor analysis and multiple regression analysis were used. The results of this study are as follows: 1. There were significant differences in self-image and pursued-image in terms of clothing purchases between women who live in Seoul and Jechon residents. 2. Demographic variables influenced to the self-image and pursued-image of clothes factor. Among them, size of the city was the most important factor which influence to the clothing purchase behavior. 3. Self-image, pursued-image of clothes, problem recognition and evaluative criteria factors significantly differed between Seoul and Jechon residents. In two cities, problem recognition factor which was arisen by external stimulus and all of the evaluative criteria factors showed significant differences. 4. When the cities were partitioned by size(large and small city), the influence of self-image and pursued-image of clothes on the clothing purchase behavior showed different phases. Generally, self image and pursued-image of clothes were more important to various problem recognition and evaluative criteria factors in large city(i.e. Seoul) than in small city(i.e. Jechon). However economic rational factor was the exception.

• Journal of Digital Contents Society
• /
• v.17 no.6
• /
• pp.523-529
• /
• 2016

Stereo matching algorithm is an essential part in computer vision and photography. Accuracy and computational complexity are challenges of stereo matching algorithm. Much research has been devoted to stereo matching based on cost volume filtering of matching costs. Local stereo matching based guided image filtering (GIF) has a computational complexity of O(N), but is still not enough to provide real-time 3-dimensional (3-D) video services. The proposed algorithm concentrates reduction of computational complexity using the concept of fast guided image filter, which increase the speed up to $O(N/\small{s}^2)$ with a sub-sampling ratio $\small{s}$. Experimental results indicated that the proposed algorithm achieves effective local stereo matching as well as a fast execution time for 3-D video service.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.21 no.10
• /
• pp.1551-1560
• /
• 1997

This study is devoted to the detection of the 3-dimensional point obstacles on the plane by using accumulated scan line images. The proposed accumulating only one scan line allow to process image at real time. And the change of motion of the feature in image is small because of the short time between image frames, so it does not take much time to track features. To obtain recursive optimal obstacles position and robot motion along to the motion of camera, Kalman filter algorithm is used. After using Kalman filter in case of the fixed environment, 3-dimensional obstacles point map is obtained. The position and motion of moving obstacles can also be obtained by pre-segmentation. Finally, to solve the stereo ambiguity problem from multiple matches, the camera motion is actively used to discard mis-matched features. To get relative distance of obstacles from camera, parallel stereo camera setup is used. In order to evaluate the proposed algorithm, experiments are carried out by a small test vehicle.

• Journal of radiological science and technology
• /
• v.41 no.5
• /
• pp.463-469
• /
• 2018

Morphological information such as shape and margin of micro lesion is important information for diagnosis of disease in clinical imaging. In this study, we investigated the morphological changes of the micro lesions by comparing the contrast and area in grid suppressed DR images according to grid frequency. In the profile analysis of the image, the mass showed an average intensity variation of 8.6 ~ 72.4 after suppression, The higher the grid frequency, the more the contrast was increased. However, in the images obtained using 103 lp / inch, which is a grid frequency less than the sampling frequency, the contrast of the mass in the vertical direction decreased after suppression. In the binary image, the area change of the mass was also large. As a result, the shape, size, and margin of the mass changed. In the case of very small calcification, the higher the grid frequency is the larger the change in contrast, so that a clear image can be obtained in the post-suppression image. However, we could confirm that the margin of the lesion was blurred and the lesion was lost in some of the images using the 103 lp / inch grid. The higher the frequency of the grid, The change of the contrast of fiber occurred largely and clear boundary was confirmed. The decrease of the number of pixels was small and morphological change was small. In conclusion, when using a grid frequency that is not suitable for the sample frequency, morphological changes or lesion loss of micro lesions in the post- suppression image may give the possibility of misdiagnosis in diagnosis and differentiation of the image.

• The KIPS Transactions:PartD
• /
• v.16D no.4
• /
• pp.613-620
• /
• 2009

It is necessary to develop a system of judging whether or not the parts are defective easily at low cost, especially in a small scale factory which manufactures a large variety of products in small amounts. To develop such system, we require to recognize objects using human's cognitive ability under various circumstances. Human's high intelligence originates mostly from neocortex of human brain. The HTM theory, which is proposed by Jeff Hopkins, is one of the recent researches to model the operation principle of neocortex. In this paper we developed PRESM (Parts image REcognition System for small scale Manufacturing industry) system based on the HTM theory to judge badness of manufactured products. As a result of application to the real field of workplace environments we identified the superiority of our recognition system.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2022.10a
• /
• pp.2-5
• /
• 2022

Human organs in the body have a complex structure, and in particular, the small intestine is about 7m long, so endoscopy is not easy and the risk of endoscopy is high. Currently, the test is performed with a capsule endoscope, and the test time is very long. The doctor connects the removed storage device to the computer to store the patient's capsule endoscope image and reads it using a program, but the capsule endoscope test results in a long image length, which takes a lot of time to read. In addition, in the case of the small intestine, there are many curves due to villi, so the occlusion area or light and shade of the image are clearly visible during the examination, and there may be cases where lesions and abnormal signs are missed during the examination. In this paper, we provide a method of assisting small intestine capsule endoscopic lesion examination using artificial neural networks to shorten the doctor's image reading time and improve diagnostic reliability.

• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.30B no.8
• /
• pp.33-40
• /
• 1993

This paper presents a hardware for image alignment based on proposed new algorithm which can align a small misaligned document image simply by one transformation with a parallel shifting of pixels. This hardware is simulated with VHDL and estimated to be about 65 ms to align an image made up of 380 by 480 pixels. Also, we will demonstrate the effectiveness of the proposed image alignment algorithm in OCR system by comparing its characteristics with those of the existing image rotation algorithms.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.42 no.7
• /
• pp.576-585
• /
• 2014

In this paper, we survey the Moving Target Indication(MTI) techniques for small UAVs. MTI consists of image alignment phase and frame differencing correction phase, and image alignment has two ways of parametric approach which is mainly focused in this paper and non-parametric approach. Since small UAVs are operated in the low altitude, the parallax is considerable and the epipolar geometry is applied to compensate the parallax. The related works and future works are presented.

• Korean Journal of Human Ecology
• /
• v.18 no.1
• /
• pp.247-257
• /
• 2009

The purpose of this study was to examine the effects of interior color and product volume of clothing shop on consumer's store image and preference for a clothing shop. The research methodology was a survey questionnaire and the subjects were 516 university students in Daejeon, Korea. The measuring instruments were stimuli and self-administrated questionnaire. The stimuli were 7 pictures of clothing shop including interior color and product volume variables, and the self-administrated questionnaire consisted of semantic differential scales for store image, store preference, and subject's demographic attribution. The data were analyzed by t-test, ANOVA, MANOVA, Duncan's multiple range test, based on SPSS program The results were as follows: first, in clothing shop's interior colors affecting store image and preference for clothing shop, white color gave a casual, sophisticated, characteristic, and attractive image to the shops, and brown color gave an elegant, sophisticated image, while black color gave a sophisticated, uncomfortable image, and gray color gave a less positive image to them than other colors. Subjects preferred white, brown, and black color in the order. Second, clothing shop's products volume also affected consumers' store image and preference. Its small volume gave a more sophisticated, elegant image than other volume levels, and subjects preferred small and medium volume of clothing products to their large volume. Third, the effects of shop's interior color and clothes' product volume on store image were different depending on subject's sex. The results revealed that clothing shop's interior color and product volume are important VMD components affecting consumer's store image and preference, and consumer's sex has to be considered to understand the effects of VMD components on clothing shop image.