• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.4
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• pp.259-266
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• 2016

Recently, CCTV cameras are emplaced actively to reinforce security and intelligent surveillance systems have been under development for detecting and monitoring of the objects in the video. In this study, we propose a method for detection of upper body in intelligent surveillance system using FCM-based RBFNN classifier realized with the aid of HOG features. Firstly, HOG features that have been originally proposed to detect the pedestrian are adopted to train the unique gradient features about upper body. However, HOG features typically exhibit a very high dimension of which is proportional to the size of the input image, it is necessary to reduce the dimension of inputs of the RBFNN classifier. Thus the well-known PCA algorithm is applied prior to the RBFNN classification step. In the computer simulation experiments, the RBFNN classifier was trained using pre-classified upper body images and non-person images and then the performance of the proposed classifier for upper body detection is evaluated by using test images and video sequences.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.11
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• pp.181-186
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• 2014

The paper proposes a new method of segmentation-based feature extraction to improve performance in pedestrian upper-body detection. General pedestrian detectors that use local features are often plagued by false positives due to the locality. Color information of multi parts of the upper body is utilized in figure-ground segmentation scheme to extract an salient, "global" shape feature capable of reducing the false positives. The performance of the multi-part color segmentation-based feature is evaluated by changing color spaces and the parameters of color histogram. The experimental result from an upper-body dataset shows that the proposed feature is effective in reducing the false positives of local feature-based detectors.

• Proceedings of the IEEK Conference
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• 2000.09a
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• pp.457-460
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• 2000

본 연구는 HWT를 이용하여 인체상반신 영상에서 얼굴부위만을 분할하기 위한 알고리즘을 제안하였다. 제안한 알고리즘은 배경을 제거하기 위하여 인체 상반신영상을 2치화 영상으로 만들고, HWT를 적용하여 평균영상과 복원영상에서 고립점, 돌출부위, 경계중복점을 제거한 후 세선화과정을 통하여 경계검출을 수행한다. 다음으로 얼굴부위의 단순경계만을 갖는 마스크를 만들고, 원영상에 마스킹하여 효과적으로 얼굴부위만을 분할한다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2013.06a
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• pp.284-285
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• 2013

Kinect를 통해 Color영상과 Depth영상을 받아온 후, 사람과 사람의 스켈레톤이 검출되도록 했다. 스켈레톤이 검출되면 머리 위치를 중심으로 얼굴 유효영역을 만들고, 효율적인 얼굴 검출로 사용자 인식이 가능하도록 했다. 스켈레톤 검출 및 추적을 통해, 4가지 감정에 대해 제스쳐를 정의했으며, 각 감정에 따른 제스쳐를 취했을 때 정의한 감정이 인식되는지 실험했다. 실험 결과, 제스쳐를 통한 감정 인식 성공률이 86~88% 나왔으며, 이 제스쳐 인식이 다른 감정인식 방법과 융합될 필요가 있다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2008.04a
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• pp.7-10
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• 2008

자동차의 수가 점점 증가함에 따라 교통사고도 그 만큼 증가하고 있다. 교통사고의 주요 원인 중 하나가 졸음운전이나 부주의한 운전에 의한 것이다. 따라서 Real-Time으로 운전자의 제스처를 인식하여 졸음운전이나 부주의에 의한 사고를 사전에 예방하여 보다 안전한 운전을 돕는 서비스가 필요시 되고 있다. 본 논문에서는 운전자의 제스처 인식에 전처리 과정으로 운전자의 상반신에 대한 영상데이터에서 Adaboost를 이용하여 복잡한 배경과 다양한 환경에서 강인하게 얼굴 영역을 찾는 알고리즘을 소개한다.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.5
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• pp.163-167
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• 2016

We have developed a millimeter-wave radiometer system for applications in the fields of medical imaging. In this paper, we introduced the brightness temperature measurement in the human body using Millimeter-wave Radiometer. Calibration of sensitivity of the radiometer system is essential to measure equivalent temperature (brightness temperature) of objects. We have developed, as a calibration source, a new type of black body for the millimeter wave region with temperature control capability. The system noise figure and temperature sensitivity of the system measured using the blackbody are 3.3 dB and 0.1 K, respectively. The brightness temperature of human body through clothes was measured to be around $38^{\circ}$[C].

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.8
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• pp.88-100
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• 1999

Detecting human face and facial landmarks automatically in an image is as essential step to a fully automatic face recognition system. In this paper, we present a new approach to detect automatically face and its eyes of input image with scale and rotation variations of faces by using an intensity based template matching with a single log-polar face template. In a template-based matching it is necessary to normalize the scale changes and rotations of an input image to a template ones. The log-polar mapping which simulates space-variant human visual system converts scale changes and rotations of input image into constant horizontal and cyclic vertical shifts in the output plane. Intelligent use of this property allows us to shift of the candidate log-polar faces mapped at various fixation points of an input image to be matched to a template over the log-polar plane. Thus, the proposed method eliminates the need of adapting multitemplate and multiresolution schemes, which inevitably give rise to intensive computation involved to cope with scale and rotation variations of faces. Through this scale and rotation involved to cope with scale and method can lead to detecting face and its eyes simultaneously. Experimental results on a database of 795 images show over 98% detection rate.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.37 no.3
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• pp.45-52
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• 2000

In this study, we propose the image segmentation algorithm for facial region segmentation. The proposed algorithm separates the mean image of low frequency band from the differential image of high frequency band in order to make a boundary using HWT, and then we reduce the isolation pixels, projection pixels, and overlapped boundary pixels from the low frequency band. Also the boundaries are detected and simplified by the proposed boundary detection algorithm, which are cleared on the thinning process of 1 pixel unit. After extracting facial image boundary by using the proposed algorithm, we make the mask and segment facial image through matching original image. In the result of facial region segmentation experiment by using the proposed algorithm, the successive facial segmentation have 95.88% segmentation value.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.3
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• pp.542-548
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• 2006

In this paper, we propose a face region detection/verification method using wavelet packet analysis and structural statistic for frontal human color image. The method extracts skin color lesions from input images, first. and then applies spatial restrictive conditions to the region, and determines whether the region is face candidate region or not. In second step, we find eye region in the face candidate region using structural statistic for standard korean faces. And in last step, the face region is verified via wavelet packet analysis if the face torture were satisfied to normal texture conditions.