• Proceedings of the Korea Information Processing Society Conference
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• 2014.04a
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• pp.861-864
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• 2014

본 논문은 혀 미각 영역별 분석을 통해 신체의 이상 여부에 대한 정보를 제공하는 설진 진단 시스템의 첫 단계로 얼굴 영상에서 혀 영역을 검출하는 실험을 통하여 미각 영역별 분석의 기반을 다진다. 제안하는 알고리즘은 혀 영상을 획득한 후, Haar-like Feature를 이용하여 혀를 검출한다. 검출된 혀 영역은 HSV컬러모델의 특징을 이용하여 이진화 한 후, Connected Component Labeling을 이용하여 혀 영역 분리한다. 한방병원의 환자들의 혀 사진 100장을 이용하여 90%의 검출률을 확인하였다.

• Journal of IKEEE
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• v.18 no.4
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• pp.536-543
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• 2014

Terrain classification is still a challenging issue in image processing, especially with high resolution satellite images. The well-known obstacles include low accuracy in the detection of targets, especially for the case of man-made structures, such as buildings and roads. In this paper, we present an efficient approach to classify and detect building footprints, foliage, grass and road from high resolution grayscale satellite images. Our contribution is to build a strong classifier using AdaBoost based on a combination of co-occurrence and Haar-like features. We expect that the inclusion of Harr-like feature improves the classification performance of the man-made structures, since Haar-like feature is extracted from corner features and rectangle features. Also, the AdaBoost algorithm selects only critical features and generates an extremely efficient classifier. Experimental result indicates that the classification accuracy of AdaBoost classifier is much higher than that of the conventional classifier using back propagation algorithm. Also, the inclusion of Harr-like feature significantly improves the classification accuracy. The accuracy of the proposed method is 98.4% for the target detection and 92.8% for the classification on high resolution satellite images.

• Smart Media Journal
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• v.5 no.1
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• pp.15-23
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• 2016

This paper proposes an effective algorithm of Korean license plate recognition. By applying Haar-like feature and Canny edge detection on a captured vehicle image, it is possible to find a connected rectangular, which is a strong candidate for license plate. The color information of license plate separates plates into white and green. Then, OTSU binary image processing and foreground neighbor pixel propagation algorithm CLNF will be applied to each license plates to reduce noise except numbers and letters. Finally, through labeling, numbers and letters will be extracted from the license plate. Letter and number regions, separated from the plate, pass through mesh method and thinning process for extracting feature vectors by X-Y projection method. The extracted feature vectors are classified using neural networks trained by backpropagation algorithm to execute final recognition process. The experiment results show that the proposed license plate recognition algorithm works effectively.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.198-201
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• 2009

In this paper, an eye tracking method is presented by using on iterated spatial moment adapting weighted gray level that can accurately detect and track user's eyes under the complicated background. The region of face is detected by using Haar-like feature before extracting region of eyes to minimize an region of interest from the input picture of CCD camera. And the region of eyes is detected by using eigeneye based on the eigenface of Principal component analysis. And then feature points of eyes are detected from darkest part in the region of eyes. The tracking of eyes is achieved correctly by using iterated spatial moment adapting weighted gray level.

• Journal of the Korea Society of Computer and Information
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• v.20 no.12
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• pp.29-36
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• 2015

In this paper, we propose the horizontal line detection using the Haar-like features and linear regression in infrared images. In the marine environment horizon image is very useful information on a variety of systems. In the proposed method Haar-like features it was noted that the standard deviation be calculated in real time on a static area. Based on the pixel position, calculating the standard deviation of the around area in real time and, if the reaction is to filter out the largest pixel can get the energy map of the area containing the straight horizontal line. In order to select a horizontal line of pixels from the energy map, we applied the linear regression, calculating a linear fit to the transverse horizontal line across the image to select the candidate optimal horizontal. The proposed method was carried out in a horizontal line detecting real infrared image experiment for day and night, it was confirmed the excellent detection results than the legacy methods.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.372-373
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• 2011

인터넷 매체가 급증함에 따라 많은 이들에게 쉽게 노출 되어 유포되고 있는 유해 영상을 검출하기 위해 다양한 분류 방법에 대한 연구들이 이루어지고 있다. 본 논문에서 유해 영상 내의 피부색 영역에서의 Haar-like 특징을 추출하여 유해 영상을 분류하는 방법을 제안한다. 이를 위해, 첫 번째 단계에는 샘플 영상에 대하여 기존에 제안된 피부색 검출 방법을 적용하고, 두 번째 단계에는 검출된 피부색 영역 내의 Haar-like 특징을 추출한다. 각 샘플 영상에서 추출한 특징들은 SVM(Support Vector Machine)을 이용하여 각각 2000 장의 유해, 무해 영상을 학습한다. 학습된 모델은 유해 및 무해 영상이 혼합되어 있는 영상 집합들을 분류하는데 사용한다.

• Journal of IKEEE
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• v.22 no.1
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• pp.46-52
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• 2018

The problem of traffic light detection and recognition has recently become one of the most important topics in various researches on autonomous driving. Most algorithms are based on colors to detect and recognize traffic light signals. These methods have disadvantage in that the recognition rate is lowered due to the change of the color of the traffic light, the influence of the angle, distance, and surrounding illumination environment of the image. In this paper, we propose machine learning based detection and recognition algorithm using shape information to solve these problems. Unlike the existing algorithms, the proposed algorithm detects and recognizes the traffic signals based on the morphological characteristics of the traffic lights, which is advantageous in that it is robust against the influence from the surrounding environments. Experimental results show that the recognition rate of the signal is higher than those of other color-based algorithms.

• 한국HCI학회:학술대회논문집
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• 2007.02a
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• pp.897-902
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• 2007

인간과 컴퓨터간의 전통적인 인터페이스는 인간이 요구하는 다양한 인터페이스를 제공하지 못한다는 점에서 점차 사용하기 불편하게 되었고 이는 새로운 형태의 인터페이스에 대한 요구로 이어지게 되었다. 본 논문에서는 이러한 추세에 맞추어 카메라를 통해 인간의 손 제스처를 인식하는 새로운 인터페이스를 연구하였다. 손은 자유도가 높고 3차원의 view direction에 의해 형상이 매우 심하게 변한다. 따라서 윤곽선 기반방법과 같은 2차원으로 투영된 영상에서 contour나 edge의 정보로 손 제스처를 인식하는 데는 한계가 있다. 그러나 모델기반 방법은 3차원 정보를 이용하기 때문에 손 제스처를 인식하는데 좋으나 계산량이 많아 실시간으로 처리하기가 쉽지 않다. 이러한 문제점을 해결하기 위해 손 형상에 대한 대규모 데이터베이스를 구성하고 정규화된 공간에서 Feature 간의 연관성을 파악하여 훈련 데이터 모델을 구성하여 비교함으로써 실시간으로 손 포즈를 구별할 수 있다. 이러한 통계적 학습 기반의 알고리즘은 다양한 데이터와 좋은 feature의 검출이 최적의 성능을 구현하는 것과 연관된다. 따라서 배경으로부터 노이즈를 최대한 줄이기 위해 피부의 색상 정보를 이용하여 손 후보 영역을 검출하고 검출된 후보 영역으로부터 HLF(Haar-like Feature)를 이용하여 손 영역을 검출한다. 검출된 손 영역으로부터 패턴 분류 과정을 거쳐 손 포즈를 인식 하게 된다. 패턴 분류 과정은 HLF를 이용하여 손 포즈를 인식하게 되는데 미리 학습된 각 포즈에 대한 HLF를 이용하여 손 포즈를 인식하게 된다. HLF는 Violar가 얼굴 검출에 적용한 것으로 얼굴 검출에 좋은 결과를 보여 주었으며, 이는 적분 이미지로부터 추출한 HLF를 이용한 Adaboost 학습 알고리즘을 사용하였다. 본 논문에서는 피부색의 색상 정보를 이용 배경과 손 영상을 최대한 분리하여 배경의 대부분이 Adaboost-Haar Classifier의 첫 번째 스테이지에서 제거되는 방법을 이용하여 그 성능을 더 향상 시켜 손 형상 인식에 적용하였다.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.3
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• pp.129-136
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• 2020

In this paper, we propose a thermal imagery-based object detection algorithm for low-light level nighttime surveillance system. Many features selected by Haar-like feature selection algorithm and existing Adaboost algorithm are often vulnerable to noise and problems with similar or overlapping feature set for learning samples. It also removes noise from the feature set from the surveillance image of the low-light night environment, and implements it using the lightweight extended Haar feature and adaboost learning algorithm to enable fast and efficient real-time feature selection. Experiments use extended Haar feature points to recognize non-predictive objects with motion in nighttime low-light environments. The Adaboost learning algorithm with video frame 800*600 thermal image as input is implemented with CUDA 9.0 platform for simulation. As a result, the results of object detection confirmed that the success rate was about 90% or more, and the processing speed was about 30% faster than the computational results obtained through histogram equalization operations in general images.

• Journal of Korea Society of Industrial Information Systems
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• v.20 no.2
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• pp.57-63
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• 2015

Compared to other emotion recognition technology, facial expression recognition technology has the merit of non-contact, non-enforceable and convenience. In order to apply to a psychological robot, vision technology must be able to quickly and accurately extract the face region in the previous step of facial expression recognition. In this paper, we remove the background from any image using the YCbCr skin color technology, and use Haar-like Feature technology for robust face detection. We got the result of improved processing speed and robust face detection by removing the background from the input image.