• 지구물리
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• 제7권4호
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• pp.247-253
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• 2004

3차원 형상화를 통한 분석이 많은 분야에서 연구 및 적용되고 있다. 3차원 형상화는 사진영상의 중첩에서 (3차원)레이저 스캐닝(laser scanning)으로 발전을 하여 가고 있으며, 각 방법이 각기 그 자체로서 발전을 해가고 있는 추세이다. 본 연구에서는 물체에 대한 데이터베이스를 구축하여 대상 이미지에 대하여 기하학적 패턴 매칭(patter matching)을 기반으로 한 인지(인식) 알고리즘을 도입하여 3차원 형상화를 통한 지질 및 지반조사를 위한 기초 기술로 활용하고자 하였다. 물체의 외형적인 성질에 기반하며 특별한 광원없이 물체를 인지할 수 있는 3차원 형상화 알고리즘은 지질 및 지반조사 분야 외에서도 많은 도움이 될 것이다.

• 한국정보디스플레이학회:학술대회논문집
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• 한국정보디스플레이학회 2008년도 International Meeting on Information Display
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• pp.761-762
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• 2008

In this paper, a novel approach for robust recognition of partially occluded 3-D target objects from computationally reconstructed integral images is proposed. The occluding object noises are selectively removed from the picked-up elemental images and performance of the proposed integral imaging-based 3-D target recognition system can be improved.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.1920-1925
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• 2004

This paper describes a new method for 3D object recognition which used surface segment-based stereo vision. The position and orientation of an objects is identified accurately enabling a robot to pick up, even though the objects are multiple and partially occluded. The stereo vision is used to get the 3D information as 3D sensing, and CAD model with its post processing is used for building models. Matching is initially performed using the model and object features, and calculate roughly the object's position and orientation. Though the fine adjustment step, the accuracy of the position and orientation are improved.

• 센서학회지
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• 제10권6호
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• pp.286-294
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• 2001

본 연구에서는 자체 제작한 3-3형 복합압전체 초음파 트랜스듀서와 SOFM(Self Organizing Feature Map) 신경회로망을 이용한 수중 3차원 물체인식특성에 대해 연구하였다. 자체 제작한 3-3형 복합압전체 소자는 수중 초음파 트랜스듀서 재료로서의 요구조건을 비교적 잘 만족하였다. 자체 제작한 3-3형 복합압전체 트랜스듀서와 SOFM 신경회로망을 이용하여 얻어진 4종의 인식물체(정사각기둥, 직사각기둥, 원통, 정삼각기둥)에 대한 전체적인 수중 물체인식률은 학습데이터인 경우에는 100%, 시험데이터는 94.0%를 나타내었다. 이들 결과로부터 자체 제작한 3-3형 복합압전체 초음파 트랜스듀서는 수중 물체인식용 트랜스듀서로서 응용될 수 있음을 알 수 있었다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1990년도 추계학술대회 논문집 학회본부
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• pp.470-473
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• 1990

In this thesis, the recognition method of the movement of an 3D object is presented. The information about the movement of a 3D object is used to recognize the object. There are 2 kinds of movements which are translation and rotation. A difference picture is obtained from a sequence of images of a moving object or a scene which is taken by a monocular stationary observer. The 3D movement of an object is recognized by the Artificial Neural Network(ANN) using the difference picture.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 B
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• pp.939-941
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• 1999

Recognition rates of 3-D underwater object using the SCL neural network and the SOFM neural network were 91.8% and 95.9%, respectively.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 2009년도 IWAIT
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• pp.535-540
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• 2009

Recently, autonomous robots which can achieve the complex tasks have been required with the advance of robotics. Advanced robot vision for recognition is necessary for the realization of such robots. In this paper, we propose a method to recognize an object in the actual environment. We assume that a 3D-object model used in our proposal method is the voxel data. Its inside is full up and its surface has color information. We also define the word "recognition" as the estimation of a target object's condition. This condition means the posture and the position of a target object in the actual environment. The proposal method consists of three steps. In Step 1, we extract features from the 3D-object model. In Step 2, we estimate the position of the target object. At last, we estimate the posture of the target object in Step 3. And we experiment in the actual environment. We also confirm the performance of our proposal method from results.

• 대한전기학회논문지:시스템및제어부문D
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• 제54권3호
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• pp.156-165
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• 2005

Human being recognizes the physical world by integrating a great variety of sensory inputs, the information acquired by their own action, and their knowledge of the world using hierarchically parallel-distributed mechanism. In this paper, authors propose the sensor fusion system that can recognize multiple 3D objects from 2D projection images and tactile informations. The proposed system focuses on improving recognition performance of 3D objects. Unlike the conventional object recognition system that uses image sensor alone, the proposed method uses tactual sensors in addition to visual sensor. Neural network is used to fuse the two sensory signals. Tactual signals are obtained from the reaction force of the pressure sensors at the fingertips when unknown objects are grasped by four-fingered robot hand. The experiment evaluates the recognition rate and the number of learning iterations of various objects. The merits of the proposed systems are not only the high performance of the learning ability but also the reliability of the system with tactual information for recognizing various objects even though the visual sensory signals get defects. The experimental results show that the proposed system can improve recognition rate and reduce teeming time. These results verify the effectiveness of the proposed sensor fusion system as recognition scheme for 3D objects.

• 한국정보통신학회논문지
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• 제13권8호
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• pp.1659-1665
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• 2009

본 논문에서는 집적영상기술을 이용한 광학적 3D 영상 상관기의 구현 방법을 제안한다. 제안하는 방법에서는 참조 3D 물체와 신호 3D 물체의 요소 영상들이 렌즈 배열을 통하여 얻어지고 이 요소 영상들을 디스플레이 패널에 다시 표시하여 출력 평면에서 광학적으로 고해상도 출력 평면 영상을 얻는다. 이렇게 얻어진 참조 및 신호 물체의 출력 평면 영상에 대하여 상호 상관관계를 수행하여 3D 물체를 인식한다. 제안한 방법은 기존 방법과 비교하여 높은 해상도의 출력 평면 영상을 사용하기 때문에 정확한 3D 물체 인식이 가능하며, 실시간 3D 물체 인식 시스템을 광학적으로 구현할 수 있다. 제안하는 방법의 유용함을 보이기 위하여 광학 실험을 수행하고 그 결과를 보고한다.

• 전자공학회논문지B
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• 제33B권5호
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• pp.60-72
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• 1996

In this paper, a enw model-base three-dimensional (3-D) object recognition mehtod using hopfield network is proposed. To minimize deformation of feature values on 3-D rotation, we select 3-D shape features and 3-D relational features which have rotational invariant characteristics. Then these feature values are normalized to have scale invariant characteristics, also. The input features are matched with model features by optimization process of hopjfield network in the form of two dimensional arrayed neurons. Experimental results on object classification and object matching with the 3-D rotated, scale changed, an dpartial oculued objects show good performance of proposed method.