• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.2379-2384
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• 2005

We propose a novel method for object recognition using the smart tag system with a stereo vision on a pan-tilt mechanism. We developed a smart tag which included IRED device. The smart tag is attached onto the object. We also developed a stereo vision system which pans and tilts for the object image to be the centered on each whole image view. A Stereo vision system on the pan-tilt mechanism can map the position of IRED to the robot coordinate system by using pan-tilt angles. And then, to map the size and pose of the object for the robot to coordinate the system, we used a simple model-based vision algorithm. To increase the possibility of tag-based object recognition, we implemented our approach by using as easy and simple techniques as possible.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 심포지엄 논문집 정보 및 제어부문
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• pp.6-8
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• 2007

In this paper, We propose reasoning system for object recognition and space classification using not only visual features but also contextual information. It is necessary to perceive object and classify space in real environments for mobile robot. especially vision based. Several visual features such as texture, SIFT. color are used for object recognition. Because of sensor uncertainty and object occlusion. there are many difficulties in vision-based perception. To show the validities of our reasoning system. experimental results will be illustrated. where object and space are inferred by bi -directional rules even with partial and uncertain information. And the system is combined with top-down and bottom-up approach.

• 한국공작기계학회논문집
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• 제16권6호
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• pp.110-116
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• 2007

This paper presents a new technique to implement the real-time recognition for shapes and model number of parts based on an active vision approach. The main focus of this paper is to apply a technique of 3D object recognition for non-contacting inspection of the shape and the external form state of precision parts based on the pattern recognition. In the field of computer vision, there have been many kinds of object recognition approaches. And most of these approaches focus on a method of recognition using a given input image (passive vision). It is, however, hard to recognize an object from model objects that have similar aspects each other. Recently, it has been perceived that an active vision is one of hopeful approaches to realize a robust object recognition system. The performance is illustrated by experiment for several parts and models.

• 제어로봇시스템학회논문지
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• 제21권10호
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• pp.958-964
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• 2015

In this paper, an object recognition method based on the depth information from the RGB-D camera, Xtion, is proposed for an indoor mobile robot. First, the RANdom SAmple Consensus (RANSAC) algorithm is applied to the point cloud obtained from the RGB-D camera to detect and remove the floor points. Next, the removed point cloud is classified by the k-means clustering method as each object's point cloud, and the normal vector of each point is obtained by using the k-d tree search. The obtained normal vectors are classified by the trained multi-layer perceptron as 18 classes and used as features for object recognition. To distinguish an object from another object, the similarity between them is measured by using Levenshtein distance. To verify the effectiveness and feasibility of the proposed object recognition method, the experiments are carried out with several similar boxes.

• 대한기계학회논문집A
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• 제28권2호
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• pp.125-132
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• 2004

In this paper, we propose a new two-stage segmentation method for the effective object recognition which uses region-growing algorithm and k-means clustering method. At first, an image is segmented into many small regions via region growing algorithm. And then the segmented small regions are merged in several regions so that the regions of an object may be included in the same region using typical k-means clustering method. This paper also establishes similarity measurement which is useful for object recognition in an image. Similarity is measured by fuzzy system whose input variables are compactness, magnitude of biasness and orientation of biasness of the object image, which are geometrical features of the object. To verify the effectiveness of the proposed two-stage segmentation method and similarity measurement, experiments for object recognition were made and the results show that they are applicable to object recognition under normal circumstance as well as under abnormal circumstance of being.

• 전자공학회논문지
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• 제53권6호
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• pp.122-129
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• 2016

컴퓨터 비전(Computer vision) 분야에서 물체인식을 위한 많은 알고리즘이 연구되고 있다. 그중 특징점(feature) 기반의 SURF(Speeded Up Robust Features) 알고리즘은 다른 알고리즘에 비해 속도와 정확도 면에서 우수하다. 하지만 SURF 알고리즘은 대응점 검출 시 대응점 오정합으로 물체인식에 실패하는 단점이 있다. 본 논문은 물체 인식률을 향상하기 위하여 SURF와 RANSAC(Random Sample Consensus) 알고리즘을 기반으로 물체인식 시스템을 구현하고, 패턴인식 필터링을 제안하였다. 또한, 실험을 통하여 물체 인식률 향상 결과를 제시하였다.

• 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.

• 한국통신학회논문지
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• 제17권3호
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• pp.197-205
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• 1992

본 논문은 신경회로망을 이용한 물체인식에 관한 연구로써, 인식은 물체의 경계점으로부터 추출된 국부 특징들로 구성되는 각 선형선소들간의 매칭에 의해 이루어진다. 그러나 추출된 특징들은 물체를 구성하는 선형선소들간의 유사성 때문에 특징 공간상에서 다른 모델과의 경계가 불분명하게 되므로 인식의 애매성이 발생하고, 특징의 유사성에 기인한 신경 회로망의 경계분리능력의 한계에 따라 인식률의 저하를 가져온다. 따라서, 본 논문에서는 인식의 애매성을 해소하고, 인식율의 향상을 도모할 수 있도록 2개의 신경회로망을 다단결합한 물체인식 시스템을 구성하였으며, 물체를 효과적으로 기술할 수 있는 국부 특징량을 사용하였다. 실험을 통하여 구성된 물체인식 시스템의 타당성을 확인하였으며, 중복 물체 및 변형된 물체에 적용하여 그 결과를 고찰하였다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2009년도 정보 및 제어 심포지움 논문집
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• pp.231-233
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• 2009

The object recognition is one of the major computer vision fields. The object recognition using features(SIFT) is finding common features in input images and query images. But the object recognition using feature methods has suffered of difficulties due to heavy calculations when resizing input images and query images. In this paper, we focused on speed up finding features in the images. we proposed method using block segmentation and histogram. Block segmentation used diving input image and than histogram decided correlation between each 1]lock and query image. This paper has confirmed that tile matching time reduced for object recognition since reducing block.

• 한국산학기술학회논문지
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• 제14권4호
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• pp.1988-1992
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• 2013

본 논문에서는 일정 지역 내에 진입한 영상 객체에 대한 객체인지 방안을 제안한다. 이 방안은 특정 지역내에 진입한 객체의 행동 패턴을 검출하고 추적하는 응용 모듈에 필요하다. 객체인지에 대한 부분은 여러 응용 모듈에서 적용될 수 있는 방안으로 단순히 영상 정보의 인식 범위에서 실제 좌표에 대한 인식으로의 확대를 위한 것이다. GPS 좌표와 영상 정보의 정합을 통하여 개체의 위치 좌표를 추출함으로서 지정 영역에서 인지된 객체의 위치를 탐색한다.