• 한국콘텐츠학회논문지
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• 제7권2호
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• pp.1-10
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• 2007

본 논문에서는 AAM(Active Appearance Model)과 가버 특징 벡터를 이용한 얼굴 인식 시스템을 제안한다. 가버 특징 벡터를 사용하는 대표적인 얼굴 인식 알고리즘인 EBGM(Elastic Bunch Graph Matching)은 가버 특징 벡터를 추출하기 위해 얼굴 특징점들의 검출을 필요로 한다. 그런데, EBGM에서 사용되는 얼굴 특징점 검출 방법은 가버젯 유사도에 기반하는데 이는 초기점에 민감하다. 잘못된 특징점 검출은 얼굴 인식에 영향을 미친다. AAM은 얼굴 특징점 검출에 효과적인 것으로 알려져 있다. 본 논문에서는 AAM으로 얼굴 특징점들을 대략적으로 추정하고 추정된 특징점들을 초기점으로 하여 가버젯 유사도 기반 특징점 검출방법으로 특징점 검출을 정교화하는 얼굴 특징점 검출 방법과 이에 기반한 얼굴 인식 시스템을 제안한다. 실험을 통해 제안된 특징점 검출 방법을 사용한 얼굴 인식 시스템이 EBGM과 같이 기존 가버젯 유사도만의 얼굴 특징점 검출을 이용한 얼굴 인식 시스템보다 더 나은 성능 개선을 보임을 실험을 통해 확인하였다.

• 항공우주기술
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• 제12권1호
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• pp.128-136
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• 2013

최근 자율 비행에 대한 관심이 증가하면서 다양한 센서를 통한 자기 위치 인식 연구가 진행되고 있다. 특히 GPS와 같은 자기 위치를 확보할 수 없는 실내 환경의 경우, 다른 방법을 통해 자기 위치를 파악해야 한다. 실내 환경에서 자기 위치 파악에는 여러 가지 방법이 있지만 영상을 통한 위치 인식 기술이 각광을 받고 있다. 본 논문에서는 마크를 통한 영상 기반의 위치 인식 연구에 대해 설명하고, 실제 비행체에 적용하여 자율 비행하는 방법에 대해 제안한다. 그리고 마크가 없는 실제 환경에서도 위치를 인식할 수 있도록 실시간 3차원 지도 생성을 통한 위치 인식 방법에 대해서도 논의한다.

• 로봇학회논문지
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• 제9권4호
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• pp.232-241
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• 2014

This paper proposes an underwater localization algorithm using probabilistic object recognition. It is organized as follows; 1) recognizing artificial objects using imaging sonar, and 2) localizing the recognized objects and the vehicle using EKF(Extended Kalman Filter) based SLAM. For this purpose, we develop artificial landmarks to be recognized even under the unstable sonar images induced by noise. Moreover, a probabilistic recognition framework is proposed. In this way, the distance and bearing of the recognized artificial landmarks are acquired to perform the localization of the underwater vehicle. Using the recognized objects, EKF-based SLAM is carried out and results in a path of the underwater vehicle and the location of landmarks. The proposed localization algorithm is verified by experiments in a basin.

• Journal of Multimedia Information System
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• 제5권3호
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• pp.179-188
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• 2018

In order to overcome the problems of contact palmprint recognition, a non-contact palmprint recognition system is developed on personal computer (PC) platform. Three methods, namely "double-line-single-point" (DLSP), "double-assistant-crosshair" (DAC) and "none-assistant-graphic" (NAG), are implemented for the palmprint localization to solve the severe technical challenges, including the complex background, variant illuminations, uncontrollable locations and gestures of hands. In NAG, hand segmentation and the cropping of region of interest are performed without any assistant graphics. The convex hull contour of hand helps detect the outside contour of little finger as well as the valley bottom between thumb and index finger. The three methods of palmprint localization have good operating efficiency and can meet the performance requirements of real-time system. Furthermore, an attendance system on PC platform is designed and developed based on non-contact palmprint recognition.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2003년도 춘계학술대회 논문집
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• pp.306-311
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• 2003

This paper presents a novel localization paradigm for mobile robots based on artificial and natural landmarks. A model-based object recognition method detects natural landmarks and conducts the global and topological localization. In addition, a metric localization method using artificial landmarks is fused to complement the deficiency of topology map and guide to action behavior. The recognition algorithm uses a modified local Zernike moments and a probabilistic voting method for the robust detection of objects in cluttered indoor environments. An artificial landmark is designed to have a three-dimensional multi-colored structure and the projection distortion of the structure encodes the distance and viewing direction of the robot. We demonstrate the feasibility of the proposed system through real world experiments using a mobile robot, KASIRI-III.

• 전기학회논문지
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• 제58권5호
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• pp.1025-1034
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• 2009

In steel making production line, steel slabs are given a unique identification number. This identification number, Slab management number(SMN), gives information about the use of the slab. Identification of SMN has been done by humans for several years, but this is expensive and not accurate and it has been a heavy burden on the workers. Consequently, to improve efficiency, automatic recognition system is desirable. Generally, a recognition system consists of text localization, text extraction, character segmentation, and character recognition. For exact SMN identification, all the stage of the recognition system must be successful. In particular, the text localization is great important stage and difficult to process. However, because of many text-like patterns in a complex background and high fuzziness between the slab and background, directly extracting text region is difficult to process. If the slab region including SMN can be detected precisely, text localization algorithm will be able to be developed on the more simple method and the processing time of the overall recognition system will be reduced. This paper describes about the slab region localization using SIFT(Scale Invariant Feature Transform) features in the image. First, SIFT algorithm is applied the captured background and slab image, then features of two images are matched by Nearest Neighbor(NN) algorithm. However, correct matching rate can be low when two images are matched. Thus, to remove incorrect match between the features of two images, geometric locations of the matched two feature points are used. Finally, search rectangle method is performed in correct matching features, and then the top boundary and side boundaries of the slab region are determined. For this processes, we can reduce search region for extraction of SMN from the slab image. Most cases, to extract text region, search region is heuristically fixed [1][2]. However, the proposed algorithm is more analytic than other algorithms, because the search region is not fixed and the slab region is searched in the whole image. Experimental results show that the proposed algorithm has a good performance.

• 한국생산제조학회지
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• 제20권2호
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• pp.133-138
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• 2011

RFID system can be used to improve object recognition, map building and localization for robot area. A novel method of indoor navigation system for a mobile robot is proposed using RFID technology. The mobile robot With a RFID reader and antenna is able to find what obstacles are located where in circumstance and can build the map similar to indoor circumstance by combining RFID information and distance data obtained from sensors. Using the map obtained, the mobile robot can avoid obstacles and finally reach the desired goal by $A^*$ algorithm. 3D map which has the advantage of robot navigation and manipulation is able to be built using z dimension of products. The proposed robot navigation system is proved to apply for SLAM and path planning in unknown circumstance through numerous experiments.

• 대한전자공학회논문지SP
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• 제46권2호
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• pp.24-29
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• 2009

음원 추정 시스템은 로봇, 화상회의, CCTV(Closed-circuit television) 시스템에 쓰인다. 이러한 음원 추정 시스템은 사람을 대상으로 하며 사람이 말하는 동안 여러 개의 음성 데이터 프레임을 입력받을 수 있다. 본 논문에서는 입력된 음성 데이터 프레임으로부터 정확한 각도를 계산 할 수 있는 음성 데이터 프레임을 선별하여 각도 추정 오차를 줄이는 방법에 대해서 제안한다. 또한 선별된 데이터를 각도로 변환한 후 메디언 필터를 적용하여 음원 추정 시스템의 오차를 줄일 수 있다. 제안된 시스템을 이용하여 실험한 후 각도 추정 오차 평균이 31%감소함을 보인다.

• 제어로봇시스템학회논문지
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• 제15권4호
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• pp.445-450
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• 2009

This paper presents a new mobile robot localization method for indoor robot navigation. The method uses hexagon distributed color-coded patches on the ceiling and a camera is installed on the robot facing the ceiling to recognize these patches. The proposed "cell-coded map", with the use of only seven different kinds of color-coded landmarks distributed in hexagonal way, helps reduce the complexity of the landmark structure and the error of landmark recognition. This technique is applicable for navigation in an unlimited size of indoor space. The structure of the landmarks and the recognition method are introduced. And 2 rigid rules are also used to ensure the correctness of the recognition. Experimental results prove that the method is useful.

• 스마트미디어저널
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• 제9권2호
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• pp.92-98
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• 2020

본 논문은 고성능의 서버 없이 안드로이드 스마트폰 단독으로 동작할 수 있도록 경량화 딥러닝 모델을 사용하여 구현한 자동차 번호판 인식 시스템을 제안한다. 자동차 번호판 인식시스템은 [번호판검출]-[문자영역 분할]-[문자인식]으로 3단계의 과정으로 구성되며, 번호판검출은 SSD-Mobilenet, 문자영역 분할은 ResNet에 localization을 추가하여 사용하였고 문자인식은 ResNet을 이용하여 구현하였다. 테스트한 기기는 삼성 갤럭시 S7, LG Q9이며 정확도는 약 85.3%, 실행속도는 약 1.1초가 소요된다.