• Journal of the Korea Society of Computer and Information
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• v.17 no.9
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• pp.49-56
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• 2012

In this paper, we propose the method that decides the shot types using face detection technique. The shot types, such as close-up shot, medium shot, and long shot, can be applied as useful information for understanding narrative structure of movies. The narrative structure of movie is builded by characters. Also their mental and emotional changes become inextricably bound up with them of narrative. The shot types are decided by distance between character and camera. If put together above them, shot types can be found by using detection technique of face size of characters and understand narrative of movie. To do this, we propose the methodology to detect shot type by face detecting and implement the system to do it. Additionally, we evaluate the performance of the system. The implementation system has been evaluated as 95% for close-up shot detection and 90% for medium shot detection, while 53.3% is just detected for long shots.

• Journal of the Korea Society of Computer and Information
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• v.13 no.1
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• pp.89-97
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• 2008

In this paper, we present a method for the enhancement of marker detection correctness and marker recognition speed by using artificial neural network. Contours of objects are extracted from the input image. They are approximated to a list of line segments. Quadrangles are found with the geometrical features of the approximated line segments. They are normalized into exact squares by using the warping technique and scale transformation. Feature vectors are extracted from the square image by using principal component analysis. Artincial neural network is used to checks if the square image is a marker image or a non-marker image. After that, the type of marker is recognized by using an artificial neural network. Experimental results show that the proposed method enhances the correctness of the marker detection and recognition.

• Proceedings of the Korean Society of Computer Information Conference
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• 2009.01a
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• pp.215-218
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• 2009

본 논문에서는 웨이블릿 변환 된 고주파 서브밴드들의 에지 정보를 이용하여 관심 객체 영역을 고속으로 자동 검출해주는 새로운 알고리즘을 제안하였다. 제안된 방법에서는 에지정보를 이용하여 블록단위의 4-방향 객체 윤곽 탐색 알고리즘(4-DOBS)을 수행하여 관심객체를 검출한다. 전체 이미지는 $64{\times}64$ 또는 $32{\times}32$ 크기의 코드 블록으로 먼저 나누어지고, 각 코드 블록 내에 에지들이 있는지 없는지에 따라 관심 코드블록 또는 배경이 된다. 4-방향은 바깥쪽에서 이미지의 중앙으로 탐색하여 접근하며, 피사계 심도가 낮은 이미지는 중앙으로 갈수록 에지가 발견된다는 특징을 이용한다. 기존 방법들의 문제점 이였던 복잡한 필터링 과정과 영역병합 문제로 인한 높은 계산도를 상당히 개선시킬 수 있었다. 또한 블록 단위의 처리로 인하여 실시간 처리를 요하는 응용에서도 적용 가능 하였다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.3
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• pp.89-97
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• 2014

In this paper, we propose real-time program that measure open-close frequency of mouth and eyes to detect drowsiness of a driver. This program detects a face to the CCD camera image using OpenCV library. Then that extracts each area using CDF for eye detection and Active Contour for mouth detection based on detected face. This system measures each frequency of Open-Close using extracted area data of eyes and mouth. We propose foundation technique how to perform sleepiness decision of users based on measurement data.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.6
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• pp.464-468
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• 2009

In this paper we propose an image-based approach, which is different from the traditional flow cytometric method to detect shape of apoptosis cells. This method can overcome the defects of cytometry and give precise recognition of apoptosis cells. In this work K-means clustering was used to do the rough segmentation and an active contour model, called 'snake' was used to do the precise edge detection. And then some features were extracted including physical feature, shape descriptor and texture features of the apoptosis cells. Finally a Mahalanobis distance classifier classifies the segmentation images as apoptosis and non-apoptosis cell.

• The Journal of the Korea Contents Association
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• v.10 no.4
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• pp.76-83
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• 2010

In this paper, we propose a method that converts a inputted real image to a image feeling like printmaking. That is, this method converts a inputted real image to man made rubber printmaking style image using image processing techniques such as spatial filters, image bit-block transfer, etc. The process is as follows. First, after detecting edges in source image, we get the first image by deleting noise lines and points, then by sharpening. Secondly, we get second image using the similar method to the first image. Finally, we blend the first and the second image by logical AND operation This processing enables us to represent rubber panel and knife effects. Also, the proposed method shows that double edge detecting is effective in enhancing line-width and removing the tiny lines.

• Journal of the Korea Computer Graphics Society
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• v.9 no.1
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• pp.11-18
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• 2003

본 논문에서는 회화적 렌더링에서 칼라변환을 이용한 브러쉬 스트로크의 생성에 관한 새로운 알고리즘을 제안한다. 본 논문의 브러쉬 스트로크 생성을 위한 전체적인 구성은 다음과 같다. 첫째, 두 장의 사진(한 장의 소스 이미지와 한 장의 참조 이미지)을 입력으로 하여 칼라 변환 이론을 적용하여 색상 테이블이 바뀐 새로운 이미지를 생성한다. 이 방법은 소스 이미지의 칼라 분포 형태를 창조 이미지의 칼라 분포 형태로 변환하기 위해, 선형 히스토그램 매칭이라 불리는, 간단한 통계학적 방법을 이용한다. 둘째, 가우시안 블러링과 소벨 필터를 이용하여 에지를 검출한다. 검출된 에지는 브러쉬 스트로크 렌더링 시 에지 부분에서 스트로크를 클리핑 함으로써 이미지의 윤곽선 보존을 위해 사용된다. 셋째, 브러쉬 스트로크의 방향을 결정하기 위한 방향맵을 생성한다. 방향맵은 입력 영상에 대한 영역 분할 및 병합을 토대로 만들어진다. 영역별 각 픽셀들에 대해 이미지 그래디언트에 기초한 일정한 방향을 부여함으로써 방향맵을 구성한다. 넷째, 구성된 방향맵을 참조하여 브러쉬 스트로크 생성의 기초가 되는 베지어 곡선(Bezier Curve)의 제어점(Control point)을 설정한다. 실제 회화작품에서 사용되는 브러쉬 스트로크는 일반적으로 곡선의 형태를 이루므로 곡선 표현이 가능한 베지어 곡선을 이용하여 브러쉬 스트로크를 표현하였다. 마지막으로, 생성된 브러쉬 스트로크를 에지부문에서 클리핑하고 배경색을 참조하여 블렌딩하거나 퐁 조명 모델을 이용하여 이미지에 적용하게 된다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.819-822
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• 2016

This paper proposes an algorithm recognizing finger using a distance graph of a detected finger's contour in a depth image. The distance graph shows angles and Euclidean distances between the center of palm and the hand contour as x and y axis respectively. We can obtain hand gestures from the graph using the fact that the graph has local maximum at the positions of finger tips. After we find the center of mass of the wrist using the fingers is thinner than the palm, we make its angle the orienting angle $0^{\circ}$. The simulation results show that the proposed algorithm can detect hand gestures well regardless of the hand direction.

• Journal of Biomedical Engineering Research
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• v.20 no.1
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• pp.59-68
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• 1999

In this paper, we propose an algorithm to locate the corpus callosum automatically from midsagittal brain MR images using the statistical characteristics and shape information of the corpus callosum. In the proposed algorithm, we first extract regions satisfying the statistical characteristics of the corpus callosum and then find a region matching the shape information. In order to match the shape information, a new directed window region-growing algorithm is proposed instead of using conventional contour matching algorithms. Using the proposed algorithm, we adaptively relax the statistical requirement until we find a region matching the shape information. Experiments show promising results.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.2
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• pp.283-290
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• 2006

The cellular neural networks have the structure that consists of an array of the same cell which is a simple processing element, and each of the cells has local connectivity and space invariant template properties. So, it has a very suitable structure for the hardware implementation. But, it is impossible to have a one-to-one mapping between the CNN hardware processors and the pixels of the practical large image. In this paper, a $5{\times}5$ CNN hardware processor with pipeline input and output that can be applied to the time-multiplexing processing scheme, which processes the large image with a small CNN cell block, is designed. the operation of the implemented $5{\times}5$ CNN hardware processor is verified from the edge detection and the shadow detection experimentations.