• Journal of Korea Multimedia Society
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• v.20 no.1
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• pp.17-31
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• 2017

The aim of the paper is to present an object recognition method toward augmented reality system that utilizes existing education instruments that was designed without any consideration on image processing and recognition. The light reflection, sizes, shapes, and color range of the existing target education instruments are major hurdles to our object recognition. In addition, the real-time performance requirements on embedded devices and user experience constraints for children users are quite challenging issues to be solved for our image processing and object recognition approach. In order to meet these requirements we employed a method cascading light-weight weak classification methods that are complimentary each other to make a resultant complicated and highly accurate object classifier toward practically reasonable precision ratio. We implemented the proposed method and tested the performance by video with more than 11,700 frames of actual playing scenario. The experimental result showed 0.54% miss ratio and 1.35% false hit ratio.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.91.3-91
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• 2001

A sensor fusion scheme for mobile robot environment recognition that incorporates range data and contour data is proposed. Ultrasonic sensor provides coarse spatial description but guarantees open space with no obstacle within sonic cone with relatively high belief. Laser structured light system provides detailed contour description of environment but prone to light noise and is easily affected by surface reflectivity. Overall fusion process is composed of two stages: Noise elimination and belief updates. Dempster Shafer´s evidential reasoning is applied at each stage. Open space estimation from sonar range measurements brings elimination of noisy lines from laser sensor. Comparing actual sonar data to the simulated sonar data enables ...

• Journal of Cadastre & Land InformatiX
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• v.52 no.1
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• pp.81-93
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• 2022

Recently, there have been many studies on object recognition around the vehicle and recognition of traffic signs and traffic lights in autonomous driving. In particular, such the recognition of traffic lights is one of the core technologies in autonomous driving. Therefore, many studies for such the recognition of traffic lights have been performed, the studies based on various deep learning models have increased significantly in recent. In addition, as a high-quality AI training data set for voice, vision, and autonomous driving is released on AIHub, it makes it possible to develop a recognition model for traffic lights suitable for the domestic environment using the data set. In this study, we developed a recognition model for traffic lights that can be used in Korea using the AIHub's training data set. In particular, in order to improve the recognition performance, we used various models of YOLOv4 and YOLOv5, and performed our recognition experiments by defining various classes for the training data. In conclusion, we could see that YOLOv5 shows better performance in the recognition than YOLOv4 and could confirm the reason from the architecture comparison of the two models.

• Journal of Convergence for Information Technology
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• v.10 no.8
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• pp.40-46
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• 2020

As interest in traffic safety increases, research on autonomous driving, which reduces the incidence of traffic accidents, is increased. Object recognition and detection are essential for autonomous driving. Therefore, research on object recognition and detection through traffic image big data is being actively conducted to determine the road conditions. However, because most existing studies use only daytime data, it is difficult to recognize objects on night roads. Particularly, in the case of a light source object, it is difficult to use the features of the daytime as it is due to light smudging and whitening. Therefore, this study proposes Yolo based light source object detection for traffic image big data processing. The proposed method performs image processing by applying color model transitions to night traffic image. The object group is determined by extracting the characteristics of the object through image processing. It is possible to increase the recognition rate of light source object detection on a night road through a deep learning model using candidate group data.

• Architectural research
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• v.22 no.4
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• pp.123-133
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• 2020

Light is an essential environmental element for elderly people to do various activities. At senior welfare centers, healthy indoor lighting is especially necessary because the facilities are used by the elderly to perform their mostly indoor activities. The purpose of this study is to evaluate light environments at senior welfare centers for well-being lighting characteristics. We based the study on the 'Biophilia' theory, a concept related to health from happiness. Thus, this study is mainly based on literary review and survey research. For this, we conducted a location focused field study to identify the current state of the lighting environments at senior welfare centers in Busan, South Korea. First, we constructed structural questionnaire to evaluate lighting environment based on 'Light and Space' biophilia theory. Then, to survey subjective evaluation, the participant of research included total of 122 senior welfare center users. Based on the results of this research, the conclusions are as follows; 1) overall, it seems that the overall result of the light environmental evaluation seems to be high because the evaluated facilities in the case survey in large-scale were recently built elderly welfare centers. 2) most of the healing design elements are focused on the introduction of natural light and psychological influence. The satisfaction with actual natural light is evaluated to be high. Although shadow and reflected light are very important in discrimination and recognition of indoor space and wayfinding, the evaluation of reflected light and shadow was low for the study. 3) items that are related to the functionality of the light were highly evaluated, while the items that are related to the spatiality of the light were rated poorly. This study has its significance when examining the effects of light environments within the welfare center form of the perspective of senior citizens. It can be referenced when reconsidering the recognition of light environment as a major consideration factor to establish a desirable senior welfare center environment.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.9 no.2
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• pp.223-228
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• 2016

A digital micromirror system was proposed for future smart learning. This system is the compact micro-projector with a built-in CMOS sensor modules. It can provide the various interfaces. The basis of interface is to recognize the finger tip on projected image. But the recognition rate of finger tip is very low due to various image degradations. In this paper, we propose the finger tip recognition algorithm that minimize the image degradation factors by using the Retinex transform and IR structuring light. By verifying the availability of the algorithm through experiment, the performance of finger tip recognition was confirmed. Therefore, the user interface can be able to be enhanced significantly in DMS.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.12
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• pp.174-182
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• 2015

In this paper, we developed the small size RGB sensor that recognizes a long distance using a low-cost color sensor. Light receiving portion of the sensor was used as a camera lens for far distance recognition, and illuminating unit was increased the strength of the light by using a high-power white LED and a lens mounted on the reflector. RGB color recognition algorithm consists of the learning process and the realtime recognition process. We obtain a normalized RGB color reference data in the learning process using the specimens painted with target colors, and classifies the three colors using the Mahalanobis distance in recognition process. We apply the developed the RGB color recognition sensor to a prototype of the part classification system and evaluate the performance of its.

• Journal of Digital Contents Society
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• v.10 no.1
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• pp.121-127
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• 2009

The automotive ECU(Electronic Control Unit) becomes more complicated and is demanding many functions. For example, many automobile companies are developing driver convenience systems such as power window switch, LCM(Light Control Module), mirror control system, seat memory. In addition, many researches and developments for DIS(Driver Information System) are in progress. It is dangerous to operate such systems in driving. In this paper, we implement the speech recognition system which controls the car convenience system using speech, and apply the preprocessing filter to improve the speech recognition rate in car noise environment. As a result, we get the good speech recognition rate in car noise environment.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.25 no.8
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• pp.568-574
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• 2015

The present study was conducted by using stereo pattern recognition method(SPR method) to measure the displacement and vibration of an airplane wing in flight condition. A SPR based measurement system was developed using two visible light stereo cameras. The visible light stereo images were processed to obtain marker points by adaptive threshold method and marker filtering technique. The marker points were used to reconstruct 3D point, displacement, and vibration data. The SPR system was installed on F-16 fighter. The wing displacement and vibration were measured in flight condition. Therefore, this paper presents a possibility that SPR based measurement system using visible light stereo camera can be very useful for measuring displacement and vibration of an airplane in flight condition.

• Journal of the Korean Society of Industry Convergence
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• v.24 no.5
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• pp.547-553
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• 2021

Dynamic object recognition is an important task for autonomous vehicles. Since dynamic objects exhibit a higher collision risk than static objects, our own trajectories should be planned to match the future state of moving elements in the scene. Time information such as optical flow can be used to recognize movement. Existing optical flow calculations are based only on camera sensors and are prone to misunderstanding in low light conditions. In this regard, to improve recognition performance in low-light environments, we applied a normalization filter and a correction function for Gamma Value to the input images. The low light quality improvement algorithm can be applied to confirm the more accurate detection of Object's Bounding Box for the vehicle. It was confirmed that there is an important in object recognition through image prepocessing and deep learning using YOLO.