• 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.

• 한국멀티미디어학회논문지
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• 제19권4호
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• pp.681-688
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• 2016

In the intelligent video surveillance, recognizing the moving objects is important issue. However, the conventional moving object recognition methods have some problems, that is, the influence of light, the distinguishing between similar colors, and so on. The recognition methods for the moving objects using depth information have been also studied, but these methods have limit of accuracy because the depth camera cannot measure the depth value accurately. In this paper, we propose a recognition method for the moving objects by using both the depth and the color information. The depth information is used for extracting areas of moving object and then the color information for correcting the extracted areas. Through tests with typical videos including moving objects, we confirmed that the proposed method could extract areas of moving objects more accurately than a method using only one of two information. The proposed method can be not only used in CCTV field, but also used in other fields of recognizing moving objects.

• 자동차안전학회지
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• 제12권4호
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• pp.23-30
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• 2020

Recently, self-driving research has been actively studied in various institutions. Accurate recognition is important because information about surrounding objects is needed for safe autonomous driving. This study mainly deals with the signal processing of LiDAR among sensors for object recognition. LiDAR is a sensor that is widely used for high recognition accuracy. First, we clustered and tracked objects by predicting relative position and speed of objects. The characteristic points of all objects were extracted using point cloud data of each objects through proposed algorithm. The Classification between vehicle and pedestrians is estimated using number of characteristic points and distances among characteristic points. The algorithm for classifying cars and pedestrians was implemented and verified using test vehicle equipped with LiDAR sensors. The accuracy of proposed object classification algorithm was about 97%. The classification accuracy was improved by about 13.5% compared with deep learning based algorithm.

• 센서학회지
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• 제33권2호
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• pp.78-85
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• 2024

We proposed a method for visual place recognition that represents images using objects as visual words. Visual words represent the various objects present in urban environments. To detect various objects within the images, we implemented and used a zero-shot detector based on a large-scale image language model. This zero-shot detector enables the detection of various objects in urban environments without additional training. In the process of creating histograms using the proposed method, frequency-based weighting was applied to consider the importance of each object. Through experiments with open datasets, the potential of the proposed method was demonstrated by comparing it with another method, even in situations involving environmental or viewpoint changes.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 학술대회 논문집 정보 및 제어부문
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• pp.107-109
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• 2004

In this paper, authors propose the sensor fusion system that can recognize multiple 3D objects from 2D projection images and tactile information. The proposed system focuses on improving recognition performance of 3D object. 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 these informations. Tactual signals are obtained from the reaction force by 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 teaming 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 visual information has a defect. The experimental results show that the proposed system can improve recognition rate and reduce learning time. These results verify the effectiveness of the proposed sensor fusion system as recognition scheme of 3D object.

• 제어로봇시스템학회논문지
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• 제14권1호
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• pp.54-61
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• 2008

This paper proposes a new algorithm for detecting and recognizing overlapped objects among a stack of arbitrarily located objects using a signature representation scheme. The proposed algorithm consists of two processes of detecting overlap of objects and of determining the boundary between overlapping objects. To determine overlap of objects, in the first step, the edge image of object region is extracted and those areas in the object region are considered as the object areas if an area is surrounded by a closed edge. For each object, its signature image is constructed by measuring the distances of those edge points from the center of the object, along the angle axis, which are located at every angle with reference to the center of the object. When an object is not overlapped, its features which consist of the positions and angles of outstanding points in the signature are searched in the database to find its corresponding model. When an object is overlapped, its features are partially matched with those object models among which the best matching model is selected as the corresponding model. The boundary among the overlapping objects is determined by projecting the signature to the original image. The performance of the proposed algorithm has been tested with the task of picking the top or non-overlapped object from a stack of arbitrarily located objects. In the experiment, a recognition rate of 98% has been achieved.

• 한국통신학회논문지
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• 제12권5호
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• pp.415-427
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• 1987

본 논문은 겹친 2차원 물체 인식과 좌표이동에 관한 실험적 시각 시스템 설계와 알고리즘에 관한 연구이다. 대상 물체는 실제 공구를 사용하였고 공구의 모양은 변하지 않으며 평편하다고 가정한다. 인식 시스템에서의 영상내의 물체의 형태는 물체의 인식에 이용되는 형태로 서술된다. 입력 데이터는 물체의 윤곽선의 순차적 배열로 감축되고 윤곽 데이터는 다각 근사화에 의해 최소한의 윤곽 꼭지점으로 줄어든다. 인식은 모델과 새로 입력된 영상과의 매칭에서 유사성을 찾는 과정이다. 다음 모델에서 겹친 물체로의 좌표이동에 의하여 은선은 재구성된다. 최상의 매칭은 유사성 검출의 최적화에 의해 얻어진다.

• 로봇학회논문지
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• 제1권2호
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• pp.172-179
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• 2006

In this paper, we present a practical place and object recognition method for guiding visitors in building environments. Recognizing places or objects in real world can be a difficult problem due to motion blur and camera noise. In this work, we present a modeling method based on the bidirectional interaction between places and objects for simultaneous reinforcement for the robust recognition. The unification of visual context including scene context, object context, and temporal context is also. The proposed system has been tested to guide visitors in a large scale building environment (10 topological places, 80 3D objects).

• 정보처리학회논문지B
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• 제13B권2호
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• pp.115-120
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• 2006

이 논문에서 겹쳐지고 잘린 이미지내의 타원형 객체들 가운데 부분적으로 겹쳐져 보이지 않는 외형과 영역을 재구성하고 계산하기 위한 방법을 제안한다. 대칭적인 속성에 기반을 두고, 불완전한 객체 인식을 위해 타원형 객체의 윤곽에 기반을 둔 방법이다. 이 방법은 한 객체 안에서 대칭 축을 이용하는 영역 복사를 통한 겹쳐져 보이지 않는 영역을 재구성하는 간결한 기교를 제공한다. 부분적으로 겹쳐져 보이지 않는 영역에 대한 측정된 변수에 기반을 두고, 분류 트리의 객체 인지를 수행하는데, 이 방법은 통계 수치보다 대칭에 기반을 둔 객체 재구성에 의존하기 때문이다. 이는 크기 변경과, 객체의 자세, 회전, 등에서 비록 객체 자세에는 한계를 가지고 있지만 부분적으로 겹쳐져 보이지 않는 객체의 인지에서 탁월하다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1992년도 한국자동제어학술회의논문집(국제학술편); KOEX, Seoul; 19-21 Oct. 1992
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• pp.573-574
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• 1992

We developed a recognition and tracking system of moving objects. The system consists of one CCD video camera, two DC motors in horizontal and vertical axles with encoders, pluse width modulation(PWM) driving unit, 16 bit NEC 9801 microcomputer, and their interfaces. The recognition and tracking system is able to recognize shape and size of a moving object and is able to track the object within a certain range of errors. This paper presents the brief introduction of the recognition and tracking system developed in our laboratory.