• 한국멀티미디어학회논문지
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• 제24권8호
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• pp.1026-1034
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• 2021

Tire wear and defect are important factors for safe driving condition. These defects are generally inspected by some specialized experts or very expensive equipments such as stereo depth camera and depth gauge. In this paper, we propose tire safety vision inspector based on deep neural network (DNN). The status of tire wear is categorized into three: 'safety', 'warning', and 'danger' based on depth of tire tread. We propose an attention mechanism for emphasizing the feature of tread area. The attention-based feature is concatenated to output feature maps of the last convolution layer of ResNet-101 to extract more robust feature. Through experiments, the proposed tire wear classification model improves 1.8% of accuracy compared to the existing ResNet-101 model. For detecting the tire defections, the developed tire defect detection model shows up-to 91% of accuracy using the Mask R-CNN model. From these results, we can see that the suggested models are useful for checking on the safety condition of working tire in real environment.

• Journal of Information Processing Systems
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• 제2권1호
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• pp.52-57
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• 2006

One of the important functions of an Intelligent Transportation System (ITS) is to classify vehicle types using a vision system. We propose a method using machine-learning algorithms for this classification problem with 3-D object model fitting. It is also necessary to detect road lanes from a fixed traffic surveillance camera in preparation for model fitting. We apply a background mask and line analysis algorithm based on statistical measures to Hough Transform (HT) in order to remove noise and false positive road lanes. The results show that this method is quite efficient in terms of quality.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 추계종합학술대회 논문집(4)
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• pp.159-162
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• 2000

In this paper, we propose an efficient method to detect shot changes in compressed MPEG video data by using reference features among video frames. The reference features among video frames imply the similarities among adjacent frames by prediction coded type of each frame. A shot change is detected if the similarity degrees of a frame and its adjacent frames are low. And the shot change detection algorithm is improved by using Fuzzy c-means (FCM) clustering algorithm. The FCM clustering algorithm uses the shot change probabilities evaluated in the mask matching of reference ratios and difference measure values based on frame reference ratios.

• Communications for Statistical Applications and Methods
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• 제22권1호
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• pp.55-67
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• 2015

An important problem in cluster analysis is the selection of variables that define cluster structure that also eliminate noisy variables that mask cluster structure; in addition, outlier detection is a fundamental task for cluster analysis. Here we provide an automated K-means clustering process combined with variable selection and outlier identification. The Automated K-means clustering procedure consists of three processes: (i) automatically calculating the cluster number and initial cluster center whenever a new variable is added, (ii) identifying outliers for each cluster depending on used variables, (iii) selecting variables defining cluster structure in a forward manner. To select variables, we applied VS-KM (variable-selection heuristic for K-means clustering) procedure (Brusco and Cradit, 2001). To identify outliers, we used a hybrid approach combining a clustering based approach and distance based approach. Simulation results indicate that the proposed automated K-means clustering procedure is effective to select variables and identify outliers. The implemented R program can be obtained at http://www.knou.ac.kr/~sskim/SVOKmeans.r.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 학술대회 논문집 전문대학교육위원
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• pp.191-193
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• 2007

Pantograph sliding plate abrasion auto-detect system, one of the electric rail car auto-detecting devices, is a system that decides how much abrasion and when to replace without an inspector physically looking at the abrasion on the wet plate using machine vision, a cutting-edge technology. This paper covers the cause of deteriorating reliability that affects pantograph wet plate edge detection due to noise added to the video when it rains. In order to remove such noise, problems should be checked through Smoothing, Averaging mask and Median filter using filtering technique and stable edge detection without being affected by noise should be induced in video measurement used in machine vision technology.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2000년도 추계학술발표논문집 (상)
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• pp.751-754
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• 2000

In this paper, we propose an efficient method to detect abrupt shot changes in compressed MPEG video data by using reference ratios among video frames. The reference ratios among video frames imply the degree of similarities among adjacent frames by prediction coded type of each frames. A shot change is detected if the similarity degrees of a frame and its adjacent frames are low. This paper proposes an efficient shot change detection algorithm by using Fuzzy c-means(FCM) clustering algorithm. The FCM clustering uses the shot change probabilities evaluated in the mask matching of reference ratios and difference measure values based on frame reference ratios.

• 대한전기학회논문지:전기물성ㆍ응용부문C
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• 제50권7호
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• pp.346-350
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• 2001

We present a surface-micromachined polysilicon capacitive accelerometer using unevenly distributed comb electrodes. The unique features of the accelerometer include a perforated proof-mass and the inner and outer comb electrodes with uneven electrode gaps. The perforated proof-mass reduces stiction between the structure and the substrate and the unevenly distributed electrodes shorten the electrode length required for a given sensitivity. The polysilicon accelerometer has been fabricated by the conventional 6-mask surface-micromachining process and showes a sensitivity of 1.03mV/g with a hybrid detection circuitry.

• 정보처리학회논문지B
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• 제8B권1호
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• pp.20-27
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• 2001

얼굴검출은 현재 많은 연구가 활발히 진행되고 있는 분야로 보안, 인식 등 다양한 응용분야를 갖는다. 본 논문은 카메라가 화자의 이동에 따라 이를 추적하여 회전하고 회의상황에 맞는 앵글을 유지하는 지능형 영상회의 시스템 개발의 기본요소인 화자검출의 선행단계로 얼굴검출에 대한 새로운 방법을 제안한다. RGB 색 공간의 입력영상을 YIQ 공간으로 변환한 후 IQ 성분은 피부영역검출에 Y 성분은 얼굴의 특성을 추출하는데 사용된다. 색 분포도를 이용하여 피부영역을 검출하고, 마스크를 누적 적용하여 잡음을 제거한 후 얼굴의 구조적인 특성과 명암의 분포를 이용하여 얼굴영역이 검출된다. 실험결과 다양한 배경의 영상에서 여러 명의 얼굴이 오류 없이 검출됨이 관찰되었다.

• 로봇학회논문지
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• 제14권1호
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• pp.1-7
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• 2019

Recently, smart factories have attracted much attention as a result of the 4th Industrial Revolution. Existing factory automation technologies are generally designed for simple repetition without using vision sensors. Even small object assemblies are still dependent on manual work. To satisfy the needs for replacing the existing system with new technology such as bin picking and visual servoing, precision and real-time application should be core. Therefore in our work we focused on the core elements by using deep learning algorithm to detect and classify the target object for real-time and analyzing the object features. We chose YOLO CNN which is capable of real-time working and combining the two tasks as mentioned above though there are lots of good deep learning algorithms such as Mask R-CNN and Fast R-CNN. Then through the line and inside features extracted from target object, we can obtain final outline and estimate object posture.

• 전자공학회논문지CI
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• 제43권4호
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• pp.58-66
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• 2006

본 논문에서는 복잡한 배경을 갖는 동영상에서 얼굴의 윤곽선을 추출하기 위해 DCM(Dilation of Color and Motion information) 마스크와 동적 윤곽선 모델 (Active Contour Models; Snakes)을 적용한다. 먼저, 얼굴의 색상 정보와 움직임 정보를 모폴로지의 팽창과 AND 연산으로 결합한 DCM 마스크를 제안하여, 복잡한 배경이 제거된 얼굴 영역을 검출하고 영상 에너지의 잡음을 제거하기 위해 사용한다. 또한, 초기 곡선에 민감한 동적 윤곽선 모델의 단점을 극복하기 위해 얼굴 요소의 기하학적인 비율에 의해 추정된 회전정도에 따라 초기곡선을 자동으로 설정하고, 에지가 약한 부분에서의 윤곽선 추출을 위해 스네이크의 영상에너지로 에지강도와 밝기를 함께 사용한다. 실험을 위해, 복잡한 배경이 있는 실내 영상과 방송 영상으로부터 양 눈이 보이는 총 16명의 다양한 헤즈 포즈 영상을 총 480장 취득하였다. 결과적으로, 얼굴의 회전정도에 따라 보간된 초기곡선을 사용하고 에지강도와 밝기의 결합 영상에너지를 사용하는 경우에 평균 처리시간은 0.28초에서 보다 정교한 얼굴 윤곽선이 추출되는 것으로 나타났다.