• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.2
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• pp.187-192
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• 2008

The Intelligent Space(ISpace) provides challenging research fields for surveillance, human-computer interfacing, networked camera conferencing, industrial monitoring or service and training applications. ISpace is the space where many intelligent devices, such as computers and sensors, are distributed. According to the cooperation of many intelligent devices, the environment, it is very important that the system knows the location information to offer the useful services. In order to achieve these goals, we present a method for representing, tracking and human Jollowing by fusing distributed multiple vision systems in ISpace, with application to pedestrian tracking in a crowd. This paper describes appearance based unknown object tracking with the distributed vision system in intelligent space. First, we discuss how object color information is obtained and how the color appearance based model is constructed from this data. Then, we discuss the global color model based on the local color information. The process of learning within global model and the experimental results are also presented.

• The KIPS Transactions:PartB
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• v.10B no.5
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• pp.535-542
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• 2003

This study is concerned with an assistive technology which reduces color blinds´s confusion when they access electronic documents including color objects in their computers. In this study, 1 restrict the assistive technology would apply to windows operating system, 256 color mode and implement to minimize color distortion which occurs in multi window environments because of color approximation process. As a basic palette, I use a 216 colors web safe palette which the Christine proposed as a standard for color blind, expand it to 256 colors to apply all computer displays using Microsoft Windows as its operating system and implement it as windows application. To test its effectiveness, I use a simulator for dichromats, as results of the test, the developed color vision deficiency correction S/W is effective to reduce the confusion. It is more effective to use the implemented S/W in both of design and client process for electronic documents.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.1
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• pp.47-59
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• 2014

This paper deals with a vision-based trajectory tracking control system for a quadrotor-type UAV for entertainment purpose in indoor environment. In contrast to outdoor flights that emphasize the autonomy to complete special missions such as aerial photographs and reconnaissance, indoor flights for entertainment require trajectory following and hovering skills especially in precision and stability of performance. This paper proposes a trajectory tracking control system consisting of a motion generation module, a pose estimation module, and a trajectory tracking module. The motion generation module generates a sequence of motions that are specified by 3-D locations at each sampling time. In the pose estimation module, 3-D position and orientation information of a quadrotor is estimated by recognizing a circular ring pattern installed on the vehicle. The trajectory tracking module controls the 3-D position of a quadrotor in real time using the information from the motion generation module and pose estimation module. The proposed system is tested through several experiments in view of one-point, multi-points, and trajectory tracking control.

• Journal of the Korea Society of Computer and Information
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• v.24 no.11
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• pp.29-39
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• 2019

As mobile robot applications increase in real life, the need of low cost autonomous driving are gradually increasing. We propose a novel vision-based real-time lane detection and tracking system that supports autonomous driving of mobile robots in constrained tracks which are designed considering indoor driving conditions of mobile robots. Considering the processing of lanes with various shapes and the pre-adjustment of operation parameters, the system structure with multi-operation modes are designed. In parameter tuning mode, thresholds of the color filter is dynamically adjusted based on the geometric property of the lane thickness. And in the unstable input mode of curved tracks and the stable input mode of straight tracks, lane feature pixels are adaptively extracted based on the geometric and temporal characteristics of the lanes and the lane model is fitted using the least-squared method. The track centerline is calculated using lane models and the motion model is simplified and tracked by a linear Kalman filter. In the driving experiments, it was confirmed that even in low-performance robot configurations, real-time processing produces the accurate autonomous driving in the constrained track.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.9
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• pp.1088-1095
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• 2019

The information and communication technology that is being developed recently has been greatly influencing the automobile market. In recent years, devices equipped with IT technology have been installed for the safety and convenience of the driver. However, it has the advantage of increased convenience as well as the disadvantage of increasing traffic accidents due to driver's distraction. In order to prevent such accidents, it is necessary to develop safety systems of various types and ways. In this paper implements a platform that can recognize LDWS and FCWS and PDWS by using a single camera without using radar sensor and camera fusion and stereo camera method using two or more sensors, and proposes to study multi-function driving safety platform using a single camera by analyzing recognition rate evaluation and validity on a vehicle.

• Journal of the Korean Data Analysis Society
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• v.20 no.6
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• pp.3127-3137
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• 2018

The study has been focused on identifying the impact of professors' types of leadership on major satisfaction, learning immersion in college students. To achieve this goal, the results of a survey of 543 affiliated students from september 2017 to november were as follows. Data processing has been done using the IBM SPSS statistics 25 program, and t-test and multi-return analysis were performed to determine the characteristics of the survey subjects, to verify the validity and reliability of the survey tools, and to identify differences and influence between factors. First, there was a difference between gender and grade, but by major, there was no difference. Second, learning immersion varies by major, gender and grade. Third, the factors that significantly influence a type of leadership's satisfaction with a major were shown to have a significant impact on vision and sense of duty, individual consideration, situational reward but were not significant in exceptional control by exception. Fourth, the impact of the types of leadership on learning immersion showed that vision and sense of duty, and situational reward, but individual consideration and exceptional control by exception were not significant.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.5
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• pp.1814-1828
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• 2021

Low-light image enhancement is a key technique to overcome the quality degradation of photos taken under scotopic vision illumination conditions. The degradation includes low brightness, low contrast, and outstanding noise, which would seriously affect the vision of the human eye recognition ability and subsequent image processing. In this paper, we propose an approach based on deep learning and Retinex theory to enhance the low-light image, which includes image decomposition, illumination prediction, image reconstruction, and image optimization. The first three parts can reconstruct the enhanced image that suffers from low-resolution. To reduce the noise of the enhanced image and improve the image quality, a super-resolution algorithm based on the Laplacian pyramid network is introduced to optimize the image. The Laplacian pyramid network can improve the resolution of the enhanced image through multiple feature extraction and deconvolution operations. Furthermore, a combination loss function is explored in the network training stage to improve the efficiency of the algorithm. Extensive experiments and comprehensive evaluations demonstrate the strength of the proposed method, the result is closer to the real-world scene in lightness, color, and details. Besides, experiments also demonstrate that the proposed method with the single low-light image can achieve the same effect as multi-exposure image fusion algorithm and no ghost is introduced.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.3904-3922
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• 2022

As a research hotspot, pedestrian detection has a wide range of applications in the field of computer vision in recent years. However, current pedestrian detection methods have problems such as insufficient detection accuracy and large models that are not suitable for large-scale deployment. In view of these problems mentioned above, a lightweight pedestrian detection and early warning method using a new model called you only look once (Yolov5) is proposed in this paper, which utilizing advantages of Yolov5s model to achieve accurate and fast pedestrian recognition. In addition, this paper also optimizes the loss function of the batch normalization (BN) layer. After sparsification, pruning and fine-tuning, got a lot of optimization, the size of the model on the edge of the computing power is lower equipment can be deployed. Finally, from the experimental data presented in this paper, under the training of the road pedestrian dataset that we collected and processed independently, the Yolov5s model has certain advantages in terms of precision and other indicators compared with traditional single shot multiBox detector (SSD) model and fast region-convolutional neural network (Fast R-CNN) model. After pruning and lightweight, the size of training model is greatly reduced without a significant reduction in accuracy, and the final precision reaches 87%, while the model size is reduced to 7,723 KB.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.120-122
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• 2021

Currently, as demand for cars at home and abroad increases, the number of vehicles is decreasing and the number of vehicles is increasing. This is the main cause of the traffic jam. To solve this problem, it operates a high-ocompancy vehicle (HOV) lane, a multi-passenger vehicle, but many people ignore the conditions of use and use it illegally. Since the police visually judge and crack down on such illegal activities, the accuracy of the crackdown is low and inefficient. In this paper, we propose a system design that enables more efficient detection using imaging techniques using computer vision to solve such problems. By improving the existing vehicle detection method that was studied, the trigger was set in the image so that the detection object can be selected and the image analysis can be conducted intensively on the target. Using the YOLO model, a deep learning object recognition model, we propose a method to utilize the shift amount of the center point rather than judging by the bounding box in the image to obtain real-time object detection and accurate signals.

• The Korean Journal of Physiology and Pharmacology
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• v.27 no.6
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• pp.541-553
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• 2023

Retinal prostheses have shown some clinical success in restoring vision in patients with retinitis pigmentosa. However, the post-implantation visual acuity does not exceed that of legal blindness. The reason for the poor visual acuity might be that (1) degenerate retinal ganglion cells (RGCs) are less responsive to electrical stimulation than normal RGCs, and (2) electrically-evoked RGC spikes show a more widespread not focal response. The single-biphasic pulse electrical stimulation, commonly used in artificial vision, has limitations in addressing these issues. In this study, we propose the benefit of multiple consecutive-biphasic pulse stimulation. We used C57BL/6J mice and C3H/HeJ (rd1) mice for the normal retina and retinal degeneration model. An 8 × 8 multi-electrode array was used to record electrically-evoked RGC spikes. We compared RGC responses when increasing the amplitude of a single biphasic pulse versus increasing the number of consecutive biphasic pulses at the same stimulus charge. Increasing the amplitude of a single biphasic pulse induced more RGC spike firing while the spatial resolution of RGC populations decreased. For multiple consecutive-biphasic pulse stimulation, RGC firing increased as the number of pulses increased, and the spatial resolution of RGC populations was well preserved even up to 5 pulses. Multiple consecutive-biphasic pulse stimulation using two or three pulses in degenerate retinas induced as much RGC spike firing as in normal retinas. These findings suggest that the newly proposed multiple consecutive-biphasic pulse stimulation can improve the visual acuity in prosthesis-implanted patients.