• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.4
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• pp.93-101
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• 2004

A face boundary can be approximated by an ellipse with five-dimensional parameters. This property allows an ellipse detection algorithm to be adapted to detecting faces. However, the construction of a huge five-dimensional parameter space for a Hough transform is quite unpractical. Accordingly, we Propose a selectively attentional Hough transform method for detecting faces from a symmetric contour in an image. The idea is based on the use of a constant aspect ratio for a face, gradient information, and scan-line-based orientation decomposition, thereby allowing a 5-dimensional problem to be decomposed into a two-dimensional one to compute a center with a specific orientation and an one-dimensional one to estimate a short axis. In addition, a two-point selection constraint using geometric and gradient information is also employed to increase the speed and cope with a cluttered background. After detecting candidate face regions using the proposed Hough transform, a multi-layer perceptron verifier is adopted to reject false positives. The proposed method was found to be relatively fast and promising.

• Journal of the Korean Society for information Management
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• v.25 no.2
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• pp.49-68
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• 2008

In many information retrieval experiments, the data fusion techniques have been used to achieve higher effectiveness in comparison to the single evidence-based retrieval. However, there had not been many image retrieval studies using the data fusion techniques especially in combining retrieval results based on multiple retrieval methods. In this paper, we describe how the image retrieval effectiveness can be improved by combining two sets of the retrieval results using the Sobel operator-based edge detection and the Self Organizing Map(SOM) algorithms. We used the clip art images from a commercial collection to develop a test data set. The main advantage of using this type of the data set was the clear cut relevance judgment, which did not require any human intervention.

• 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 Broadcast Engineering
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• v.23 no.2
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• pp.246-252
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• 2018

Hand gestures are attracting attention as a NUI (Natural User Interface) of wearable devices such as smart glasses. Recently, to support efficient media consumption in IoT (Internet of Things) and wearable environments, the standardization of IoMT (Internet of Media Things) is in the progress in MPEG. In IoMT, it is assumed that hand gesture detection and recognition are performed on a separate device, and thus provides an interoperable interface between these modules. Meanwhile, deep learning based hand gesture recognition techniques have been recently actively studied to improve the recognition performance. In this paper, we propose a method of hand gesture recognition based on CNN (Convolutional Neural Network) for various applications such as media consumption in wearable devices which is one of the use cases of IoMT. The proposed method detects hand contour from stereo images acquisitioned by smart glasses using depth information and color information, constructs data sets to learn CNN, and then recognizes gestures from input hand contour images. Experimental results show that the proposed method achieves the average 95% hand gesture recognition rate.

• Journal of Digital Convergence
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• v.12 no.8
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• pp.337-343
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• 2014

This paper proposes a minimal contour tracking algorithm that reduces transmission of data for tracking mobile objects in surveillance networks in terms of detection and communication load. This algorithm perform detection for object tracking and when it transmit image data to server from camera, it minimized communication load by reducing quantity of transmission data. This algorithm use minimal tracking area based on the kinematics of the object. The modeling of object's kinematics allows for pruning out part of the tracking area that cannot be mechanically visited by the mobile object within scheduled time. In applications to detect an object in real time,when transmitting a large amount of image data it is possible to reduce the transmission load.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.10
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• pp.156-165
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• 1998

In this paper, the hybrid algorithm for the scene change detection of MPEG-based compressed video data is proposed. There have been two methods to detect scene changes of video data compressed using algorithms such as MPEG or motion-JPEG: analyzing the compressed data directly, and analyzing from the retrieved data. The former has the advantage of taking less time, while the latter can obtain detail results at the expense of time and memory. Thus by combining each algorithm we detect cuts from compressed sequence, retrieve data for some selected region, and detect gradual scene changes. Simulation results verify the superiorities of the proposed algorithm in analyzing time and accuracy.

• Proceedings of the IEEK Conference
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• 2000.09a
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• pp.623-626
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• 2000

본 논문에서는 얼굴의 특징 추출을 이용해서 캐리커쳐를 자동으로 생성하는 알고리즘을 제안한다. 제안된 방법은 사진이나 카메라를 이용해서 입력된 영상으로부터 색상정보를 이용하여 얼굴영역을 검출하고 얼굴의 기하학적인 구조를 이용해서 유전자 알고리즘의 추정 파라미터를 설정하여 최적의 특징 점의 위치를 검출한다. 검출된 특징 점 위치를 이용하여 눈, 코, 입, 눈썹, 머리카락 등 얼굴의 특징이 되는 구성요소를 추출한다. 마지막으로 얼굴의 윤곽선을 구한 다음 추출된 얼굴의 구성요소들을 합성하여 간단하면서도 개인의 특징을 잘 반영할 수 있는 캐리커쳐를 생성한다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2016.06a
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• pp.338-339
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• 2016

손 제스처는 스마트 글래스 등 웨어러블 기기의 NUI(Natural User Interface)로 부각되고 있으며 이를 위해서는 손 제스처 검출 및 인식 기능이 요구된다. 또한, 최근 MPEG 에서는 IoT(Internet of Thing) 환경에서의 미디어 소비를 위한 표준으로 IoMTW(Media-centric IoT and Wearable) 사전 탐색이 진행되고 있으며, 손 제스처를 표현하기 위한 메타데이터도 하나의 표준 기술요소로 논의되고 있다. 본 논문에서는 스마트 글래스 환경에서의 손 제스처 인식을 위한 과정으로 스테레오 영상을 통한 손 윤곽선 검출과 이를 메타데이터로 서술하기 위하여 베지에(Bezier) 곡선으로 표현하는 기법을 제시한다.

• Proceedings of the Korea Contents Association Conference
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• 2009.05a
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• pp.79-82
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• 2009

In this paper, we propose new image processing method to detect fire image. At captured image from camera, we using OpenCV library to implement various image processing techniques such like differential image, binarization image, contour extraction, remove noise(morphology open, close), pixel calculation, flickering extraction, etc.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.10a
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• pp.440-443
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• 2008

적목 현상은 야간에 플래시를 이용하여 촬영 시 나타나는 현상으로, 대부분 미숙한 사진 촬영 방법으로 인해 많이 발생한다. 이러한 적목 현상은 수정 시에 이미지 틀을 이용하여 제거해야 되며, 이러한 방법은 많은 시간과 기술이 요구된다. 따라서 본 논문에서는 적목 현상이 일어난 영역을 자동으로 추출하여 제거하는 방법을 제안한다. 적목 영역은 RGB 레벨의 영상을, 각각 YCbCr과 HSI 컬러 공간으로 변환 후에 사람의 피부색 정보를 이용하여 얼굴 영역을 검출한다. 검출된 얼굴 영역에서 적목이 존재하는 눈 영역은 색상 정보와 8 방향 윤곽선 추적 방법을 적용하여 적목 영역을 검출한 후에 적목 현상을 제거한다. 제안된 적목 영역 추출 및 제친 방법을 적목 현상이 나타나는 30장의 영상을 대상으로 실험한 결과, 제안된 방법이 적목 영역 제거에 효과적임을 확인하였다.