• The Journal of Korean Institute of Next Generation Computing
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• v.15 no.5
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• pp.31-42
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• 2019

With the recent development of deep learning technology, remarkable achievements have been made in many research areas of computer vision. Deep learning has also made dramatic improvement in two-dimensional or three-dimensional human pose estimation based on a single image, and many researchers have been expanding the scope of this problem. The human pose estimation is one of the most important research fields because there are various applications, especially it is a key factor in understanding the behavior, state, and intention of people in image or video analysis. Based on this background, this paper surveys research trends in estimating human poses based on a single image. Because there are various research results for robust and accurate human pose estimation, this paper introduces them in two separated subsections: 2D human pose estimation and 3D human pose estimation. Moreover, this paper summarizes famous data sets used in this field and introduces various studies which utilize human poses to solve their own problem.

• Journal of Web Engineering
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• v.21 no.3
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• pp.729-750
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• 2022

In general, the importance of 6DoF (degree of freedom) 3D (dimension) volumetric contents technology is emerging in 5G (generation) telepresence service, Web-based (WebGL) graphics, computer vision, robotics, and next-generation augmented reality. Since it is possible to acquire RGB images and depth images in real-time through depth sensors that use various depth acquisition methods such as time of flight (ToF) and lidar, many changes have been made in object detection, tracking, and recognition research. In this paper, we propose a method to improve the quality of 3D models for 5G telepresence by processing images acquired through depth and RGB cameras on a multi-view camera system. In this paper, the quality is improved in two major ways. The first concerns the shape of the 3D model. A method of removing noise outside the object by applying a mask obtained from a color image and a combined filtering operation to obtain the difference in depth information between pixels inside the object were proposed. Second, we propose an illumination compensation method for images acquired through a multi-view camera system for photo-realistic 3D model generation. It is assumed that the three-dimensional volumetric shooting is done indoors, and the location and intensity of illumination according to time are constant. Since the multi-view camera uses a total of 8 pairs and converges toward the center of space, the intensity and angle of light incident on each camera are different even if the illumination is constant. Therefore, all cameras take a color correction chart and use a color optimization function to obtain a color conversion matrix that defines the relationship between the eight acquired images. Using this, the image input from all cameras is corrected based on the color correction chart. It was confirmed that the quality of the 3D model could be improved by effectively removing noise due to the proposed method when acquiring images of a 3D volumetric object using eight cameras. It has been experimentally proven that the color difference between images is reduced.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.3
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• pp.271-278
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• 2014

In spite of that stop line and crosswalk should be aware of the most basic objects in transportation system, its features extracted are very limited. In addition to image-based recognition technology, laser and RF, GPS/INS recognition technology, it is difficult to recognize. For this reason, the limited research in this area has been done. In this paper, the algorithm to recognize the stop line and crosswalk is designed and implemented using image-based recognition technology with the images input through a vision sensor. This algorithm consists of three functions.; One is to select the area, in advance, needed for feature extraction in order to speed up the data processing, 'Region of Interest', another is to process the images only that white color is detected more than a certain proportion in order to remove the unnecessary operation, 'Color Pattern Inspection', the other is 'Feature Extraction and Recognition', which is to extract the edge features and compare this to the previously-modeled one to identify the stop line and crosswalk. For this, especially by using case based feature comparison algorithm, it can identify either both stop line and crosswalk exist or just one exists. Also the proposed algorithm is to develop existing researches by comparing and analysing effect of in-vehicle camera installation and changes in recognition rate of distance estimation and various constraints such as backlight and shadow.

• Journal of Internet of Things and Convergence
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• v.10 no.2
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• pp.85-91
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• 2024

In this paper, we studied projection mapping, which is being utilized as a digital canvas beyond space and time for theme parks, mega events, and exhibition performances. Since the existing projection technology used for fixed objects has the limitation that it is difficult to map moving objects in terms of utilization, it is urgent to develop a technology that can track and map moving objects and a real-time dynamic projection mapping system based on dynamically moving objects so that it can respond to various markets such as performances, exhibitions, and theme parks. In this paper, we propose a system that can track real-time objects in real time and eliminate the delay phenomenon by developing hardware and performing high-speed image processing. Specifically, we develop a real-time object image analysis and projection focusing control unit, an integrated operating system for a real-time object tracking system, and an image processing library for projection mapping. This research is expected to have a wide range of applications in the technology-intensive industry that utilizes real-time vision machine-based detection technology, as well as in the industry where cutting-edge science and technology are converged and produced.

• 한국노년학
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• v.40 no.4
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• pp.729-746
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• 2020

For the development and better use of information and communication technology(ICT) for promoting health and quality of life in older people, it is important to understand personal factors associated with ICT use. The aims of this study were to describe the pattern of ICT use according to health characteristics of elderly and to investigate significant factors related to ICT use among three age groups. The data of 2017 National Survey of Older Koreans was used for analysis and 10,058 of older adults aged 65 and over were included for study participants. As results, first, the young old(65~74) used more functions of ICT than the middle-old(75~84) and the old-old(85 and over). Second, ICT was more used among older people with good conditions in chronic disease, subjective health, cognition, depression, vision, hearing, walking and IADL. Third, higher prevalence of ICT use was significantly associated with age, education, household income, subjective health, depression, cognitive function, vision limitation and walking limitation. Fourth, factors influencing the use of ICT were a little different among three age groups. For the young-old(65~74), subjective health, depression, cognitive decline, walking limitation, and IADL disability were significant factors affecting on ICT use. For the middle-old(75-84), subjective health, depression, cognitive decline, vision, hearing, and walking limitation were related with their ICT use. For the old-old(85 and over), only cognitive function and depression were associated with their ICT use. Based on these results, this study suggested the need to consider multiple health conditions for developing and using ICT for older adults.

• Journal of Korean Society for Geospatial Information Science
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• v.21 no.3
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• pp.19-25
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• 2013

Recently, more accurate database information of facilities is being required, with the increase in importance of urban road facility management. Therefore, this study proposed how to automatically detect particular traffic signs necessary for efficient construction of road facility DB. For this study, central locations of facilities were searched, after recognition and automatic detection of particular traffic signs through an image. Then, coordinate values of traffic signs calculated in the study were compared with real coordinate values, in order to evaluate the accuracy of traffic sign locations which were finally detected. Computer vision technology was used in recognizing and detecting traffic signs through OPEN CV-based coding, and photogrammetry was used in calculating accurate locations of detected traffic signs. For the experiment, circular road signal(No Parking) and triangular road signal(Crosswalk) were chosen out of various kinds of road signals. The research result showed that the circular road signal had a nearly 50cm error value, and the triangular road signal had a nearly 60cm error value, when comparing the calculated coordinates with the real coordinates. Though this result is not satisfactory, it is considered that there would be no problem to find locations of traffic signs.

• The Journal of the Convergence on Culture Technology
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• v.10 no.5
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• pp.323-330
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• 2024

He reality is that the college life culture of college students is gradually changing as we enter the AI era. In addition, in the aftermath of the MZ generation's employment difficulties and unemployment difficulties, freshmen begin to worry about their future from college entrance. Therefore, it is necessary to design a college life that can lead to employment from a careful plan for an efficient life in college. This study attempted to suggest a direction for college freshmen to find their career direction, starting with self-exploration, and helping them settle down in college life. In addition, a program was developed with a focus on understanding others and communicating human relationships toward fostering communities, starting with self-understanding. Weeks 1 to 5 were composed of getting to know each other, weeks 6 to 10 were made up of me, we, and weeks 11 to 15 were made up of our future. First, a program was developed so that you can understand yourself and plan your own career path based on this, present your own vision, and specify your future to draw a blueprint.We expect that the research results will contribute to the maturation of career attitudes of students participating in college life planning classes, smooth communication, reduction of school and department dropout rates, and encouragement of satisfaction and pride in school.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.39 no.4
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• pp.429-441
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• 2002

This paper concerns extracting 3D motion information from a 3D input device in real time focused to enabling effective human-computer interaction. In particular, we develop a novel algorithm for extracting 6 degrees-of-freedom motion information from a 3D input device by employing an epipolar geometry of stereo camera, color, motion, and structure information, free from requiring the aid of camera calibration object. To extract 3D motion, we first determine the epipolar geometry of stereo camera by computing the perspective projection matrix and perspective distortion matrix. We then incorporate the proposed Motion Adaptive Weighted Unmatched Pixel Count algorithm performing color transformation, unmatched pixel counting, discrete Kalman filtering, and principal component analysis. The extracted 3D motion information can be applied to controlling virtual objects or aiding the navigation device that controls the viewpoint of a user in virtual reality setting. Since the stereo vision-based 3D input device is wireless, it provides users with a means for more natural and efficient interface, thus effectively realizing a feeling of immersion.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.4
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• pp.358-365
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• 2017

In this paper, we propose an object recognition method using image information to improve the efficiency of visual servoingfor industrial manipulators in industry. This is an image-processing method for real-time responses to an abnormal situation or to external environment change in a work object by utilizing camera-image information of an industrial manipulator. The object recognition method proposed in this paper uses the Otsu method, a thresholding technique based on separation of the V channel containing color information and the S channel, in which it is easy to separate the background from the HSV channel in order to improve the recognition rate of the existing Harris Corner algorithm. Through this study, when the work object is not placed in the correct position due to external factors or from being twisted,the position is calculated and provided to the industrial manipulator.

• The Journal of the Korea Contents Association
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• v.21 no.6
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• pp.701-715
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• 2021

The drastic advance of recent deep learning technologies is heavily dependent on training datasets which are essential to train models by themselves with less human efforts. In comparison with the work to design deep learning models, preparing datasets is a long haul; at the moment, in the domain of vision intelligent, datasets are still being made by handwork requiring a lot of time and efforts, where workers need to directly make labels on each image usually with GUI-based labeling tools. In this paper, we overview the current status of vision datasets focusing on what datasets are being shared and how they are prepared with various labeling tools. Particularly, in order to relieve the repetitive and tiring labeling work, we present an interactive smart image annotating system with which the annotation work can be transformed from the direct human-only manual labeling to a correction-after-checking by means of a support of automatic labeling. In an experiment, we show that automatic labeling can greatly improve the productivity of datasets especially reducing time and efforts to specify regions of objects found in images. Finally, we discuss critical issues that we faced in the experiment to our annotation system and describe future work to raise the productivity of image datasets creation for accelerating AI technology.