• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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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.

• Proceedings of the KIEE Conference
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• 2007.04a
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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.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.16 no.6
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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.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.10
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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.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.28 no.2
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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.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.6
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• pp.122-129
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• 2016

There have been a lot of researches on object recognition in computer vision. The SURF(Speeded Up Robust Features) algorithm based on feature detection is faster and more accurate than others. However, this algorithm has a shortcoming of making an error due to feature point mismatching when extracting feature points. In order to increase a success rate of object recognition, we have created an object recognition system based on SURF and RANSAC(Random Sample Consensus) algorithm and proposed the pattern recognition filtering. We have also presented experiment results relating to enhanced the success rate of object recognition.

• Proceedings of the KIEE Conference
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• 2004.11c
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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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.17 no.3
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• pp.197-205
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• 1992

In this paper object recognition using neural network is studied. The recognition is accomplished by matching linear line segments which are formed by local features extracted from the curvature points. Since there is similarities among segments. The boundary of models is not distinct in feature space. Due to these indistinctness the ambiguity of recognition occurs, and the recognition rate becomes degraded according to the limitation of boundary decision capability of neural network for similar of features. Object recognition and to improve recognition rate. Local features are used to represent the object effectively. The validity of the object recognition system is demonstrated by experiments for the occluded and varied objects.

• Proceedings of the IEEK Conference
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• 2009.05a
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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.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.4
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• pp.1988-1992
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• 2013

In this paper, we propose a method of object recognition to real image object which enter into an area. We needs this method for an application module to detect and trace the moving pattern of some objects entered into an specific area. A scheme to the object recognition is adopted to some applied modules that it is moved from only real image information recognition to real coordination recognition, the mapping between the GPS coordination and real image information provides object coordination.