• Proceedings of the KIEE Conference
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• 1998.07g
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• pp.2246-2248
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• 1998

To control the remote system in the unstructured environment requires data under certain circumstances. When a machine is dealt with an unstructured environment, new environment structure is to be composed. The stereo vision system can get both the intensity data and the range data. So, in this paper, data architecture of a stereo image is proposed to set them.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.952-955
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• 2015

본 연구는 현재 스마트폰 모델을 조립하는 공정에서 카메라 렌즈 부품을 조립하는 수동으로 팔레트 위에 위치한 부품의 좌표를 수동으로 입력하고 검증하는 과정으로 소형 델타로봇에 스마트 액추에이터와 초소형 비전 카메라를 장착하여 렌즈부품의 위치좌표를 자동으로 검출하고, 빠르고 정확하게 타겟팅된 위치로 이동시킬 수 있는 자동타게팅 델타로봇 시스템을 개발하는 것이다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.47-50
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• 2023

본 논문에서는 딥러닝 CNN 알고리즘을 이용하여 사람의 얼굴 이미지를 학습한 다음 나이와 성별을 예측하는 시스템을 제안한다. 이 시스템은 개개인 마다 각기 다른 외형적 특성을 고려하여 이를 분석한 다음 이에 맞는 헤어 스타일, 옷차림을 추천할 수 있다. 해당 기술을 활용하여 메타버스 아바타 생성에 사용자의 얼굴과 같은 신체적 특성을 고려할 수 있다. 향후에는 신체 전체를 이미지화하여 보다 더 다양한 정보를 인식할 수 있도록 연구를 진행할 것이다.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.1104-1105
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• 2023

조선업계에서 전문 인력 수급난으로 난항을 겪고 있다. 이는 선박의 검사에 차질을 빚었고 해양 오염과 선박사고와 같은 문제가 발생하고 있다. 이에 안전 검진 수행에 AI 이미지 인식 기반 진단 모델을 적용하여, 애플리케이션을 통해 비전문가도 품질 진단을 수행할 수 있도록 한다.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.6
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• pp.1099-1110
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• 2023

The recognition system of autonomous driving and robot navigation performs vision work such as object recognition, tracking, and lane detection after multi-sensor fusion to improve performance. Currently, research on a deep learning model based on the fusion of a camera and a lidar sensor is being actively conducted. However, deep learning models are vulnerable to adversarial attacks through modulation of input data. Attacks on the existing multi-sensor-based autonomous driving recognition system are focused on inducing obstacle detection by lowering the confidence score of the object recognition model.However, there is a limitation that an attack is possible only in the target model. In the case of attacks on the sensor fusion stage, errors in vision work after fusion can be cascaded, and this risk needs to be considered. In addition, an attack on LIDAR's point cloud data, which is difficult to judge visually, makes it difficult to determine whether it is an attack. In this study, image scaling-based camera-lidar We propose an attack method that reduces the accuracy of LCCNet, a fusion model (camera-LiDAR calibration model). The proposed method is to perform a scaling attack on the point of the input lidar. As a result of conducting an attack performance experiment by size with a scaling algorithm, an average of more than 77% of fusion errors were caused.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.1
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• pp.1-6
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• 2014

In this study, we propose optimized RBFNNs based on night vision face recognition simulator with the aid of $(2D)^2$ PCA algorithm. It is difficult to obtain the night image for performing face recognition due to low brightness in case of image acquired through CCD camera at night. For this reason, a night vision camera is used to get images at night. Ada-Boost algorithm is also used for the detection of face images on both face and non-face image area. And the minimization of distortion phenomenon of the images is carried out by using the histogram equalization. These high-dimensional images are reduced to low-dimensional images by using $(2D)^2$ PCA algorithm. Face recognition is performed through polynomial-based RBFNNs classifier, and the essential design parameters of the classifiers are optimized by means of Differential Evolution(DE). The performance evaluation of the optimized RBFNNs based on $(2D)^2$ PCA is carried out with the aid of night vision face recognition system and IC&CI Lab data.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.42 no.4 s.304
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• pp.11-18
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• 2005

For expressing intention the human often use body languages as well as vocal languages. Of course the gestures using arms and hands are the representative ones among the body languages. Therefore it is very important to understand the human arm motion in human-computer interaction. In this respect we present here how to estimate 3D pose of human arms by using computer vision systems. For this we first focus on the idea that the human arm motion consists of mostly revolute joint motions, and then we present an algorithm for understanding 3D motion of a revolute joint using vision systems. Next we apply it to estimating 3D pose of human arms using vision systems. The fundamental idea for this algorithm extension is that we may apply the algorithm for a revolute joint to each of the revolute joints of hmm arms one after another. In designing the algorithms we focus on seeking closed-form solutions with high accuracy because we aim at applying them to human computer interaction for ubiquitous computing and virtual reality.

• The KIPS Transactions:PartB
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• v.14B no.4
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• pp.311-320
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• 2007

This paper presents vision-based 3D facial expression animation technique and system which provide the robust 3D head pose estimation and real-time facial expression control. Many researches of 3D face animation have been done for the facial expression control itself rather than focusing on 3D head motion tracking. However, the head motion tracking is one of critical issues to be solved for developing realistic facial animation. In this research, we developed an integrated animation system that includes 3D head motion tracking and facial expression control at the same time. The proposed system consists of three major phases: face detection, 3D head motion tracking, and facial expression control. For face detection, with the non-parametric HT skin color model and template matching, we can detect the facial region efficiently from video frame. For 3D head motion tracking, we exploit the cylindrical head model that is projected to the initial head motion template. Given an initial reference template of the face image and the corresponding head motion, the cylindrical head model is created and the foil head motion is traced based on the optical flow method. For the facial expression cloning we utilize the feature-based method, The major facial feature points are detected by the geometry of information of the face with template matching and traced by optical flow. Since the locations of varying feature points are composed of head motion and facial expression information, the animation parameters which describe the variation of the facial features are acquired from geometrically transformed frontal head pose image. Finally, the facial expression cloning is done by two fitting process. The control points of the 3D model are varied applying the animation parameters to the face model, and the non-feature points around the control points are changed by use of Radial Basis Function(RBF). From the experiment, we can prove that the developed vision-based animation system can create realistic facial animation with robust head pose estimation and facial variation from input video image.

• Journal of Convergence for Information Technology
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• v.7 no.1
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• pp.61-66
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• 2017

As the fourth industrial revolution becomes more popular and advanced services such as artificial intelligence and Internet of Things technology are widely commercialized, awareness of the importance of software is spreading. Recently, software education has been taught not only in elementary school and college but also in college. Also, there is a growing interest in computational thinking needed to solve problems through computing methodology and model. The purpose of this study is to design an app inventor course for non-majors software education based on computational thinking. As a result of the study, six detailed competencies of computational thinking were derived, and six detailed competencies were mapped to the app inventor learning elements. In addition, based on the computational thinking modeling, I designed an app inventor class for students who participated in IT curriculum of university liberal arts curriculum.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.1
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• pp.225-231
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• 2013

In this study, we propose the design of optimized pRBFNNs-based night vision face recognition system using PCA algorithm. It is difficalt to obtain images using CCD camera due to low brightness under surround condition without lighting. The quality of the images distorted by low illuminance is improved by using night vision camera and histogram equalization. Ada-Boost algorithm also is used for the detection of face image between face and non-face image area. The dimension of the obtained image data is reduced to low dimension using PCA method. Also we introduce the pRBFNNs as recognition module. The proposed pRBFNNs consists of three functional modules such as the condition part, the conclusion part, and the inference part. In the condition part of fuzzy rules, input space is partitioned by using Fuzzy C-Means clustering. In the conclusion part of rules, the connection weights of pRBFNNs is represented as three kinds of polynomials such as linear, quadratic, and modified quadratic. The essential design parameters of the networks are optimized by means of Differential Evolution.