• Journal of the Korean Geophysical Society
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• v.7 no.4
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• pp.247-253
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• 2004

In this paper, 3D recognizing algorithm which is based on the external shape feature is presented. Since many objects have the regular shape, if we posses the database of pattern and we recognize the object using the database of the object's pattern, it is possible to inspect and/or recognize the objects of many fields. This paper handles on the 3D object recognition algorithm using the geometrical pattern matching by 3D database.

• 한국정보디스플레이학회:학술대회논문집
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• 2008.10a
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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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• 2004.08a
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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.

• Journal of Sensor Science and Technology
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• v.10 no.6
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• pp.286-294
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• 2001

In this study, 3-D underwater object recognition using the self-made 3-3 type composite ultrasonic transducer and modified SOFM(Self Organizing Feature Map) neural network are investigated. Properties of the self-made 3-3 type composite specimens are satisfied considerably with requirements as an underwater ultrasonic transducer's materials. 3-D underwater all object's recognition rates obtained from both the training data and testing data in different objects, such as a rectangular block, regular triangular block, square block and cylinderical block, were 100% and 94.0%, respectively. All object's recognition rates are obtained by utilizing the self-made 3-3 type composite transducer and SOFM neural network. From the object recognition rates, it could be seen that an ultrasonic transducer fabricated with the self-made 3-3 type composite resonator will be able to have application for the underwater object recognition.

• Proceedings of the KIEE Conference
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• 1990.11a
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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.

• Proceedings of the KIEE Conference
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• 1999.07b
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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.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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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.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.3
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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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.8
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• pp.1659-1665
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• 2009

In this paper, we propose an implementation method of 3D image correlator using integral imaging technique. In the proposed method, elemental images of the reference and signal 3D objects are recorded by lenslet arrays and then reference and signal output plane images with high resolution are optically reconstructed on the output plane by displaying these elemental images into a display panel. Through cross-correlations between the reconstructed reference and the single plane images, 3D object recognition is performed. The proposed method can provide a precise 3D object recognition by using the high-resolution output plane images compared with the previous methods and implement all-optical structure for real-time 3D object recognition system. To show the feasibility of the proposed method, optical experiments are carried out and the results are presented.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.5
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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.