• Proceedings of the KIEE Conference
• /
• 2001.07d
• /
• pp.2424-2427
• /
• 2001

마이크로 매니퓰레이션에서는 제어에 활용 할 수 있는 센싱 정보의 제약, 마이크로 영역 특유의 물리현상 등으로 인하여 원하는 제어 성능을 달성하는데 큰 어려움이 따른다. 마이크로 영역의 여러 센싱 정보 중에서 특히 비젼 정보를 잘 활용하면 이러한 제약을 크게 완화할 수 있을 뿐만 아니라 실시간 영상처리를 통해 페루프 제어도 가능하다. 대부분의 마이크로 비젼 시스템에서는 초점 심도가 낮고 초점 영역의 선택에 제약이 따르기 때문에 종횡비가 큰 3차원 마이크로 부품을 인식하고 조작하는데 큰 어려움이 있다. 본 논문에서는 3차원 마이크로 부품의 조작을 위해, 초점 영역의 선택이 가능한 마이크로 비전 시스템을 제안하고 실험을 통하여 그 유용성을 실증하였다.

• Korean Journal of Optics and Photonics
• /
• v.22 no.1
• /
• pp.10-15
• /
• 2011

In this paper, we propose an auto-focusing algorithm which extracts a depth parameter by analyzing a selected part of a hologram, and we use experimental results to show that the algorithm is practical. First, we record a complex hologram using Optical Scanning Holography. Next we select some part of hologram and extract depth information through Gaussian low pass filtering, synthesizing a real-only hologram, power fringe-adjusted filtering and inverting to a new frequency axis. Finally, we reconstruct the hologram automatically using the extracted depth location.

• Proceedings of the Acoustical Society of Korea Conference
• /
• autumn
• /
• pp.379-382
• /
• 1999

모든 문장에는 '새로운' 정보를 전달하기 위한 초점이 있고 높낮돋들림을 포함하는 초점범위는 다시 정보 초점을 필수 요소로 갖는 정보 구조 경계를 갖는다. 모호성이 없는 적절한 초점 구조를 결정하기 위해 '국어 초점 원리'를 도입함으로써 초점 성분의 영역이 확인되고 화맥에 의한 초점 해석이 가능해진다. 초점 성분을 설명하고 높낮돋들림과 초점 돋들림의 관계를 기술하는 '기본초점규칙'이 필요하며 '정보 구조 원리'에 의해 '새로운' 정보가 선택되어 초점 범위는 화맥에 의해 구체화된다. 정보 구조가 문법 체계의 모든 의미 계층과 관계를 가지며 정보 구조의 경계 안에 정보 초점으로 실현되는 초점 돋들림이 있게 되므로 기본 초점 규칙은 '초점 돋들림 원리'로 수정되어 초점 범위 내의 음절에 초점 돋들림이 할당된다.

• Journal of the Institute of Convergence Signal Processing
• /
• v.10 no.4
• /
• pp.233-238
• /
• 2009

In this paper, we propose the methods to select the stable region for the detect region which is required in the system used the face to the ROI in the auto-focus digital camera. this method regards the face region as the ROI in the progressive input frame and focusing the region in the mobile camera embeded ISP module automatically. The learning algorithm to detect the face is the Adaboost algorithm. we proposed the method to detect the slanted face not participate in the train process and postprocessing method for the results of detection, and then we proposed the stabilization method to sustain the region not shake for the region. we estimated the capability for the stabilization algorithm using the RMS between the trajectory and regression curve.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.48 no.2
• /
• pp.80-87
• /
• 2011

In this paper we present subject region-based auto-focusing algorithm using noise robust focus measure. The proposed algorithm automatically estimates the main subject using entropy and solves the traditional problems with a subject position or high frequency component of background image. We also propose a new focus measure by analyzing the discrete cosine transform coefficients. Experimental results show that the proposed method is more robust to Gaussian and impulse noises than the traditional methods. The proposed algorithm can be applied to Pan-tilt-zoom (PTZ) cameras in the intelligent video surveillance system.

• Journal of Digital Convergence
• /
• v.10 no.3
• /
• pp.173-178
• /
• 2012

In this paper, a new saliency map generation method is discussed which extracts objects effectively using extracted Salient Region. Feature map is constructed first using four features of edge, hue of HSV color model, focus and entropy and then conspicuity map is generated from Center Surround Differences using the feature map. Final saliency map is constructed by the combination of conspicuity maps. Saliency map generated using this procedure is compared to the conventional technique and confirmed that new technique has better results.

• Proceedings of the Korean Information Science Society Conference
• /
• 2001.04a
• /
• pp.592-594
• /
• 2001

본 논문은 절차지향 소프트웨어로부터 클래스와 상속성을 추출하기 위한 방법론을 제안한다. 본 논문에서 제안한 방법론은 모든 경우의 클래스 후보군과 그들의 상속성을 생성하여 클래스 후보군과 영역 모델 사이의 관계성과 유사 정도를 가지고 최고 또는 최적의 클래스 후보군을 선택하는데 초점을 둔다. 클래스와 상속성 추출 방법론은 다음과 같은 두드러진 특징을 가지고 있다: 정적(속성)과 동적(메소드)인 클러스터링 방법을 사용하고, 클래스 후보군의 경우는 추상화에 초점을 두며, m개의 클래스 후보와 n개의 클래스 후보 사이의 상속 관계의 유사도 측정 즉, 2차원적 유사도 측정은 m개의 클래스 후보와 n개의 클래스 후보 사이의 전체 그룹에 대한 유사도를 구하는 수평적 측정과 클래스 후보군들에서 상속성을 가진 클래스의 집합과 영역 모델에서 같은 클래스 상송성을 가진 클래스 집합사이의 유사도를 위한 수직적 측정방법이 있다. 이러한 방법론은 최고 또는 최적의 클래스 후보군을 선택하기 위해 제공학 전문가에게 광범위하고 통합적인 환경을 제시하고 있다.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.43 no.2 s.308
• /
• pp.30-39
• /
• 2006

Iris recognition is that identifies a user based on the unique iris texture patterns which has the functionalities of dilating or contracting pupil region. Iris recognition systems extract the iris pattern in iris image captured by iris recognition camera. Therefore performance of iris recognition is affected by the quality of iris image which includes iris pattern. If iris image is blurred, iris pattern is transformed. It causes FRR(False Rejection Error) to be increased. Optical defocusing is the main factor to make blurred iris images. In conventional iris recognition camera, they use two kinds of focusing methods such as lilted and auto-focusing method. In case of fixed focusing method, the users should repeatedly align their eyes in DOF(Depth of Field), while the iris recognition system acquires good focused is image. Therefore it can give much inconvenience to the users. In case of auto-focusing method, the iris recognition camera moves focus lens with auto-focusing algorithm for capturing the best focused image. However, that needs additional H/W equipment such as distance measuring sensor between users and camera lens, and motor to move focus lens. Therefore the size and cost of iris recognition camera are increased and this kind of camera cannot be used for small sized mobile device. To overcome those problems, we propose method to increase DOF by iris image restoration algorithm based on focus value of iris image. When we tested our proposed algorithm with BM-ET100 made by Panasonic, we could increase operation range from 48-53cm to 46-56cm.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.13 no.12
• /
• pp.2547-2554
• /
• 2009

In this paper, we proposed a face detection algorithm and a hardware implementation method for ROI(Region Of Interest) of AF(Auto Focusing). We used face features in skin regions of YCbCr color space for face detection. The face features are the number of skin pixels in face regions, edge pixels in eye regions, and shadow pixels in lip regions. The each feature was statistically selected by 2,000 sample pictures of face. The proposed algorithm detects two faces that are closer center of the image for considering the effectiveness of hardware resource. The detected faces are displayed by rectangle for ROI of AF, and the rectangles are represented by positions in the image about starting point and ending point of the rectangles. The proposed face detection method was verified by using FPGA boards and mobile phone camera sensor.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.17 no.4
• /
• pp.494-499
• /
• 2007

In this paper we realized a system, if a user selects the area of the important parts or the arrangement parts when he reads the books or the papers, which amends, stores and readjusts the characters that are included in the selected area by outputting the characters to the word processor in sequence. If a user selects what he wishes lot with his finger, the system detects the movement of the finger by applying the hand recognition algorithm and recognizes the selected area. The system converts the distance of the width and the length of the selected area to the number of the pulse, and controls the motor to move the camera at the position. After the system scales up/down the zoom to be able to recognize the character and controls the focus to the regulated zoom closely, it controls the focus in detail to get more distinct image by using the difference of the light and darkness. We realize the recognition and editing support system of korean language that converts the obtained images to the document by applying the character recognition algorithm and arrange the important parts.