• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.2
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• pp.11-20
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• 2010

In this paper we propose a face detection system which consists of a method of face candidate extraction using skin color and a method of face verification using the feature of facial structure. Firstly, the proposed extraction method of face candidate uses the image segmentation and merging algorithm in the regions of skin color and the neighboring regions of skin color. These two algorithms make it possible to select the face candidates from the variety of faces in the image with complicated backgrounds. Secondly, by using the partial face classifier, the proposed face validation method verifies the feature of face structure and then classifies face and non-face. This classifier uses face images only in the learning process and does not consider non-face images in order to use less number of training images. In the experimental, the proposed method of face candidate extraction can find more 9.55% faces on average as face candidates than other methods. Also in the experiment of face and non-face classification, the proposed face validation method obtains the face classification rate on the average 4.97% higher than other face/non-face classifiers when the non-face classification rate is about 99%.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.1
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• pp.10-19
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• 2003

Caption information in news videos can be useful for video indexing and retrieval since it usually suggests or implies the contents of the video very well. In this paper, a new algorithm for extracting and recognizing characters from news video is proposed, without a priori knowledge such as font type, color, size of character. In the process of text region extraction, in order to improve the recognition rate for videos with complex background at low resolution, continuous frames with identical text regions are automatically detected to compose an average frame. The image of the averaged frame is projected to horizontal and vertical direction, and we apply region filling to remove backgrounds to produce the character. Then, K-means color clustering is applied to remove remaining backgrounds to produce the final text image. In the process of character recognition, simple features such as white run and zero-one transition from the center, are extracted from unknown characters. These feature are compared with the pre-composed character feature set to recognize the characters. Experimental results tested on various news videos show that the proposed method is superior in terms of caption extraction ability and character recognition rate.

• 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.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.8
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• pp.8-15
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• 1993

This paper describes a normalization of character which is contained in the character recognition process. Line and dot density is computed on input character image and then image mapping is executed into destination. Also recognition is processed using overlap-partitioning of character image and extraction of 4 directional feature primitives. The validity of proposed nonlinear normalization algorithm could be verified by increment of recognition rate.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2001.06a
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• pp.181-184
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• 2001

본 논문에서는 얼굴영상의 특징추출에 적합한 LITFE (Linear Interpolated Triangle Feature Extraction)를 이용하여 얼굴영상을 인식하는 알고리즘을 제안한다. LITFE는 얼굴의 위치정보를 보존하면서 영상 분할이 가능한 특징추출 알고리즘으로, PCA (Principal Component Analysis) 의 신경회로망적 접근방법인 GHA(Genralized Hebbian Algorithm)와 병행하면 얼굴의 특징을 효과적으로 추출하여 인식할 수 있는 장점이 있다.

• 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.

• Journal of Electrical Engineering and information Science
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• v.3 no.3
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• pp.342-347
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• 1998

An OptiNeural system is developed for optical pattern classification. It is a novel hybrid system which consists of an optical processor and a multilayer neural network. It takes advantages of two dimensional processing capability of an optical processor and nonlinear mapping capability of a neural network. The optical processor with a binary phase only filter is used as a preprocessor for feature extraction and the neural network is used as a decision system through mapping. OptiNeural system is trained for optical pattern classification by use of a simulated annealing algorithm. Its classification performance for grey tone texture patterns is excellent, while a conventional optical system shows poor classification performance.

• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2549-2551
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• 2003

We propose character recognition system for Gerber files. The Gerber file is the vector-formatted drawing file for PCB manufacturing. To consider the special vector format and rotated characters, we develop segmentation and feature extraction method. The modularized neural network is then applied to the recognition algorithm. Finally, comparative simulation results are presented to verify the usefulness of the proposed method.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.238-240
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• 2003

As remote sensing is weighty in GIS updating, it is indispensable to get spatial information quickly and exactly. In this study, we have designed and implemented the program by two algorithms of GDPA (Gradient Direction Profile Analysis) and Hough transformation to extract linear features automatically from high-resolution imagery. We applied the software to embody both algorithms to KOMPSAT-EOC, IKONOS, and Landsat-ETM and made a comparative study of results.

• Journal of the Institute of Convergence Signal Processing
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• v.9 no.4
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• pp.272-279
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• 2008

Shot change detection is an important technique for effective management of video data, so detection scheme requires adaptive detection techniques to be used actually in various video. In this paper, we propose an adaptive shot change detection algorithm using the mean of feature value on variable reference blocks. Our algorithm determines shot change detection by defining adaptive threshold values with the feature value extracted from video frames and comparing the feature value and the threshold value. We obtained better detection ratio than the conventional methods maximally by 15% in the experiment with the same test sequence. We also had good detection ratio for other several methods of feature extraction and could see realtime operation of shot change detection in the hardware platform with low performance was possible by implementing it in TVUS model of HOMECAST company. Thus, our algerian in the paper can be useful in PMP(portable multimedia player) or other portable players.