• Proceedings of the IEEK Conference
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• 대한전자공학회 1999년도 추계종합학술대회 논문집
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• pp.597-600
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• 1999

In this paper, we propose a fast object-tracking algorithm in a moving picture. The proposed object-tracking algorithm is based on a projection scheme. More specifically, to alleviate the computational complexities of the previous motion estimation methods, we propose to use the projected row and column 1-D image data to extract the motion information. Experimental results show that the proposed method can detect the motion of an object fairly well with reduced computational time.

• Proceedings of the IEEK Conference
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• 대한전자공학회 2003년도 하계종합학술대회 논문집 Ⅳ
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• pp.2120-2123
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• 2003

This paper presents the moving object detection and tracking algorithm using edge information base on human perceptual system The human visual system recognizes shapes and objects easily and rapidly. It's believed that perceptual organization plays on important role in human perception. It presents edge model(GCS) base on extracted feature by perceptual organization principal and extract edge information by definition of the edge model. Through such human perception system I have introduced the technique in which the computers would recognize the moving object from the edge information just like humans would recognize the moving object precisely.

• Proceedings of the IEEK Conference
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• 대한전자공학회 2002년도 하계종합학술대회 논문집(4)
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• pp.141-144
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• 2002

This paper describes a new feature based tracking system that can track moving objects with a pan-tilt camera. We extract corner features of the scene and tracks the features using filtering, The global motion energy caused by camera movement is eliminated by finding the maximal matching position between consecutive frames using Pyramidal template matching. The region of moving object is segmented by clustering the motion trajectories and command the pan-tilt controller to follow the object such that the object will always lie at the center of the camera. The proposed system has demonstrated good performance for several video sequences.

• KIPS Transactions on Software and Data Engineering
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• 제3권5호
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• pp.195-200
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• 2014

Typical methods of active contour model for object contour extraction are snake and level. Snake is usually faster than level set, but has limitation to compute topology of objects. Level set on the other hand is slower but good at it. In this paper, a new object contour extraction algorithm to use advantage of each is proposed. The algorithm is composed of two main steps. In the first step, snake is used to extract the rough contour and then in the second step, level set is applied to extract the complex contour exactly. 5 binary images and 2 natural images with different contours are simulated by a proposed algorithm. It is shown that speed is reduced and contour is better extracted.

• Journal of the Korea Academia-Industrial cooperation Society
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• 제9권1호
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• pp.100-107
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• 2008

This paper proposes a real-time processing method to simplify the image input procedure while extracting and tracing a specified object among multiple ones. In order to extract an object in a specified area among multiple objects of indoor environment and tracing the extracted object continuously, it is verified through experiments that the information interchanged between cameras upwards and in front of it have effect on tracing a specified object continuously. The camera located upward transfers its x-axis data of the input image to the front camera so that the front camera can catch the area of object soon without computing the information of x-axis. The front camera can't resolve the problem of objects overlapping till they share information with the upward camera. The result of the experiment shows that the computation for tracing an object is simplified and the accuracy for extracting and tracing is upgraded.

• Journal of Advanced Navigation Technology
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• 제14권2호
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• pp.272-277
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• 2010

AR(Augmented Reality) needs medium between real and virtual, world, and recognition techniques are necessary to track an object continuously. Optical tracking using marker is mainly used, but it takes time and is inconvenient to attach marker onto the target objects. Therefore, many researchers try to develop markerless tracking techniques nowaday. In this paper, we extract features and 3D position from 3D objects and suggest realtime tracking based on these features and positions, which do not use just coplanar features and 2D position. We extract features using SURF, get rotation matrix and translation vector of 3D object using POSIT with these features and track the object in real time. If the extracted features are nor enough and it fail to track the object, then new features are extracted and re-matched to recover the tracking. Also, we get rotation in matrix and translation vector of 3D object using POSIT and track the object in real time.

• The Transactions of The Korean Institute of Electrical Engineers
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• 제64권7호
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• pp.1047-1055
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• 2015

We proposes a method for improving the identification and tracking of the moving objects in intelligent video surveillance system. The proposed method consists of 3 parts: object detection, object recognition, and object tracking. First of all, we use a GMM(Gaussian Mixture Model) to eliminate the background, and extract the moving object. Next, we propose a labeling technique forrecognition of the moving object. and the method for identifying the recognized object by using the optical flow and EMD algorithm. Lastly, we proposes method to track the location of the identified moving object regions by using location information of moving objects and Kalman filter. Finally, we demonstrate the feasibility and applicability of the proposed algorithms through some experiments.

• The Transactions of the Korea Information Processing Society
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• 제5권3호
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• pp.649-661
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• 1998

If there is redeveloping into the system applying the object-oriented paradigm, productivity Improvement of software through reuse would be accomplished and maintenance cost be reduced. When a procedure-oriented system is transformed to a type applying the object-oriented paradigm, various techniques are studied to extract objects from source code automatically or semi-automatically. However, it is not easy to extract conceptuat objects when those techniques are applied, This problem entails another problem which drops the conceptual integrity of the extracted objects. In this paper, we suggest an object extraction method and tool development to resolve the problem occurring at the time when thc pr"~r"m, dcveloped through procedure-oriented is transformed to an object-oriented system. The suggested method allow to extract the desired objects using object modeling for various application domains of the real world given source code and design recovery information. During the extraction process, functionality and global variables of the source code as well as its intcrface arc rigorously analyzed. This process can enhance the conceptual integrity of the objects and make easy to construct class hierarchies.

• Journal of Advanced Navigation Technology
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• 제11권1호
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• pp.59-63
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• 2007

In this paper, an image segmentation algorithm is described to extract both the contour line and the inner grid patterns of body in case of ambiguous environment. The binary method using a threshold is used to extract image boundary. To reduce image noise, the $3{\times}3$ hybrid high pass filter adjusted for applying 3D information extraction of complicated shape object is proposed. This hybrid high pass filter algorithm can be applied to extract complicated shape object such as 3D body shape, CAD system, and factory automation since the processing time for image denoising is shorter than the conventional methods.

• The Journal of the Korea institute of electronic communication sciences
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• 제8권3호
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• pp.409-414
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• 2013

With the increase of multimedia information such as images, researches have been realized on how to extract the high-level semantic information from low-level visual information, and a variety of techniques have been proposed to generate this information automatically. However, most of these technologies extract the semantic information between single images, it's difficult to extract semantic information when a combination of multiple objects within the image. In this paper, we extract the visual features of objects within the image and training images stored in the DB and the features of each object are defined by measuring the similarity. Using ontology reasoner, each object feature within images infers the semantic information by positional relation and associative relation. With this, it's possible to infer semantic information between objects within images, we proposed a method for inferring more complicated and a variety of high-level semantic information.