• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2036-2038
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• 2001

In this paper, we propose the neural network based emotion recognition method for intelligently recognizing the human's emotion using vision system. In the proposed method, human's emotion is divided into four emotion (surprise, anger, happiness, sadness). Also, we use R,G,B(red, green, blue) color image data and the gray image data to get the highly trust rate of feature point extraction. For this, we propose an algorithm to extract four feature points (eyebrow, eye, nose, mouth) from the face image acquired by the color CCD camera and find some feature vectors from those. And then we apply back-prapagation algorithm to the secondary feature vector(position and distance among the feature points). Finally, we show the practical application possibility of the proposed method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.3
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• pp.1418-1433
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• 2019

In computer graphics, 3D mesh segmentation is a challenging research field. This paper presents a 3D mesh model segmentation algorithm that focuses on removing exterior salient parts from the original 3D mesh model based on prominent feature points and marching plane. To begin with, the proposed approach uses multi-dimensional scaling to extract prominent feature points that reside on the tips of each exterior salient part of a given mesh. Subsequently, a set of planes intersect the 3D mesh; one is the marching plane, which start marching from prominent feature points. Through the marching process, local cross sections between marching plane and 3D mesh are extracted, subsequently, its corresponding area are calculated to represent local volumes of the 3D mesh model. As the boundary region of an exterior salient part generally lies on the location at which the local volume suddenly changes greatly, we can simply cut this location with the marching plane to separate this part from the mesh. We evaluated our algorithm on the Princeton Segmentation Benchmark, and the evaluation results show that our algorithm works well for some categories.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.5-7
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• 1996

We propose a triangulation method based on stereo vision angles. We setup stereo vision systems which extract the depth information to a moving object by detecting a moving object using difference image method and obtaining the depth information by the triangulation method based on stereo vision angles. The feature point of a moving object is used the geometrical center of the moving object, and the proposed vision system has the accuracy of 0.2mm in the range of 400mm.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.10a
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• pp.90-93
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• 2004

본 논문에서는 고정점 알고리즘의 독립성분분석을 이용한 물체영상의 특징추출을 제안하였다. 여기서 고정점 알고리즘은 뉴우턴법에 기초한 것으로 빠른 특징추출성능을 얻기 위함이고, 독립성분분석의 이용은 통계적으로 독립인 기저영상을 효과적으로 추출하기 위함이다. 제안된 기법을 Image＊after사에서 제공하는 352$\times$264 픽셀의 10개 물체영상을 대상으로 실험한 결과, 빠르면서도 정확한 복원성능과 PCA보다도 개선된 특징 추출성능이 있음을 확인하였다.

• Proceedings of the Korean Information Science Society Conference
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• 2007.10a
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• pp.231-232
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• 2007

• Proceedings of the Korean Society of Computer Information Conference
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• 2012.01a
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• pp.37-40
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• 2012

본 논문에서는 지문 인식 시스템의 인식을 위한 등록점으로 융선의 단점과 분기점에 관하여 연구하였다. 원 지문 영상은 전처리 과정을 거치게 되면서 잘못된 특징점을 포함하게 되며 이는 지문 인식 시스템의 효율성을 감소시키는 원인이 될 수 있다. 이 논문에서는 세선화된 지문 영상으로부터 후보 특징점을 추출한 후 연결성 탐색 정보를 이용하여 의사 특징점을 제거할 수 있는 알고리즘을 제안한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.5
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• pp.1856-1869
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• 2015

This paper addresses the issues of 3D human activity detection, tracking and recognition from RGB-D video sequences using a feature structured framework. During human tracking and activity recognition, initially, dense depth images are captured using depth camera. In order to track human silhouettes, we considered spatial/temporal continuity, constraints of human motion information and compute centroids of each activity based on chain coding mechanism and centroids point extraction. In body skin joints features, we estimate human body skin color to identify human body parts (i.e., head, hands, and feet) likely to extract joint points information. These joints points are further processed as feature extraction process including distance position features and centroid distance features. Lastly, self-organized maps are used to recognize different activities. Experimental results demonstrate that the proposed method is reliable and efficient in recognizing human poses at different realistic scenes. The proposed system should be applicable to different consumer application systems such as healthcare system, video surveillance system and indoor monitoring systems which track and recognize different activities of multiple users.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.1
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• pp.185-191
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• 2011

Recently, the surveillance system is highly being attention. Various Technologies as detecting object from image than determining and recognizing if the object are person are universally being used. Therefore, In this paper shows detecting on this kind of object and local context based facial feather detection algorithm is being advocated. Detect using Gabor Bunch in the same time Bayesian detection method for revision to find feather point is being described. The entire system to search for object area from image, context-based face detection, feature extraction methods applied to improve the performance of the system.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.8
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• pp.55-67
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• 1998

This paper presents an automatic facial feature point extraction algorithm in sequential color images. To extract facial region in the video sequence, a moving color detection technique is proposed that emphasize moving skin color region by applying motion detection algorithm on the skin-color transformed images. The threshold value for the pixel difference detection is also decided according to the transformed pixel value that represents the probability of the desired color information. Eye candidate regions are selected using both of the black/white color information inside the skin-color region and the valley information of the moving skin region detected using morphological operators. Eye region is finally decided by the geometrical relationship of the eyes and color histogram. To decide the exact feature points, the PCA(Principal Component Analysis) is used on each eye and mouth regions. Experimental results show that the feature points of eye and mouth can be obtained correctly irrespective of background, direction and size of face.

• Information and Communications Magazine
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• v.10 no.10
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• pp.22-29
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• 1993

This paper describes development of a real-time voice recognition dialing system which can recognize around one hundred word vocabularies in speaker independent mode. The voice recognition algorithm is implemented on a DSP board with a telephone interface plugged in an IBM PC AT/486. In the DSP board, procedures for feature extraction, vector quantization(VQ), and end-point detection are performed simultaneously in every 10msec frame interval to satisfy real-time constraints after the word starting point detection. In addition, we optimize the VQ codebook size and the end-point detection procedure to reduce recognition time and memory requirement. The demonstration system is being displayed in MOBILAB of Korea Mobile Telecom at the Taejon EXPO '93.