• Journal of Korea Multimedia Society
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• v.7 no.12
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• pp.1657-1664
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• 2004

We propose a method that classifies images into two object types man-made and natural objects. A central object is extracted from each image by using central object extraction method[1] before classification. A central object in an images defined as a set of regions that lies around center of the image and has significant color distribution against its surrounding. We define three measures to classify the object images. The first measure is energy of edge direction histogram. The energy is calculated based on the direction of only non-circular edges. The second measure is an energy difference along directions in Gabor filter dictionary. Maximum and minimum energy along directions in Gabor filter dictionary are selected and the energy difference is computed as the ratio of the maximum to the minimum value. The last one is a shape of an object, which is also represented by Gabor filter dictionary. Gabor filter dictionary for the shape of an object differs from the one for the texture in an object in which the former is computed from a binarized object image. Each measure is combined by using majority rule tin which decisions are made by the majority. A test with 600 images shows a classification accuracy of 86%.

• JSTS:Journal of Semiconductor Technology and Science
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• v.16 no.2
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• pp.251-254
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• 2016

An energy-efficient object matching accelerator is proposed for mobile object recognition based on matching prediction scheme. Conventionally, vocabulary tree has been used to save the external memory bandwidth in object matching process but involved massive internal memory transactions to examine each object in a database. In this paper, a novel object matching accelerator is proposed based on matching predictions to reduce unnecessary internal memory transactions by mitigating non-target object examinations, thereby improving the energy-efficiency. Experimental results show a 26% reduction in power-delay product compared to the prior art.

• The Journal of Information Technology and Database
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• v.7 no.1
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• pp.17-26
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• 2000

To provide highly available services in the distributed object system, it is required to support the object group. The state machine approach and primary-backup approach are proposed as two representative approaches for support of object group. The primary-backup approach does not only give merits such as transparency of object group and non-deterministic execution but also require less resource than state machine approach. This paper describes an extension of CORBA that is required to support of the primary-backup object group. In this paper, the state of backup is synchronized with primary through the atomic multicast protocol whenever the request of client is executed at primary. As a result, it does not require message logging and check pointing. The object group of this paper also provides fast response time in case of failure of the primary since it makes primary election unnecessary. And through an extension of IDL, it makes possible to avoid consistency control depending on characteristic of application. A prototype has been implemented and the performance of object group has been compared with a single object invocation.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.2 no.1
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• pp.23-28
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• 2009

Recently, the research which concerns the object class recognition has been done. Although an object tracking based on most of histograms employs a colored model to improve robustness, the system is not reliable enough yet. In this paper, we presents a method to express and track an object by using the histograms which are composed with visual features through succesive frames. The experimental results shows that this method is reliable to track a car within 80m distance from camera.

• Proceedings of the Korea Society of Information Technology Applications Conference
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• 2005.11a
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• pp.251-253
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• 2005

In this paper, an adaptive algorithm is proposed in streaming MPEG-4 contents with fluctuating resource amount such as throughput of network conditions. MPEG-4 is the international standard for audiovisual presentation which is composed of object based media streams. The proposed technique provides the media stream corresponding an object with multiple media streams with different qualities and bit rate in order to support object based scalability to the MPEG-4 content. In addition, making the object streams adaptable, a feasible stream set selected from the multiple streams for transmission with optimal quality in the form of the current status.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.4 no.3
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• pp.158-166
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• 2011

Generally, moving object tracking used Lucas-Kanade feature tracking method which is strong in movement, rotation and size. But this method is very weak of occlusion by background or another object and so on. In this case, this method tracks backgrounds or another objects instead a moving object, or a tracking is finished. In order to solve this problem, we proposes Lucas-Kanade feature tracking method which introduce a destimation function and prediction function.

• Electronics and Telecommunications Trends
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• v.33 no.4
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• pp.33-42
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• 2018

Object classification and detection are fundamental technologies in computer vision and its applications. Recently, a deep-learning based approach has shown significant improvement in terms of object classification and detection. This report reviews the progress of deep-learning based object classification and detection in views of the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), and analyzes recent trends of object classification and detection technology and its applications.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.8
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• pp.766-770
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• 2010

In this paper, we present an effective covariance tracking algorithm based on adaptive size changing of tracking window. Recent researches have advocated the use of a covariance matrix of object image features for tracking objects instead of the conventional histogram object models used in popular algorithms. But, according to the general covariance tracking algorithm, it can not deal with the scale changes of the moving objects. The scale of the moving object often changes in various tracking environment and the tracking window(or object kernel) has to be adapted accordingly. In addition, the covariance matrix of moving objects should be adaptively updated considering of the tracking window size. We provide a solution to this problem by segmenting the moving object from the background pixels of the tracking window. Therefore, we can improve the tracking performance of the covariance tracking method. Our several simulations prove the effectiveness of the proposed method.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.492-497
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• 2009

Computer-Generated Hologram (CGH) is made for three dimensional image of a virtual object. Error diffusion method is used for the phase quantization of CGH, and it is known to be effective to the image quality improvement of the reconstructed image. However, the image quality of the reconstructed image from the CGH using error diffusion method depends on the selection of error diffusion coefficient. In this paper, we derived the relational expression to obtain the error diffusion coefficient from the position of the input object and size of the input object for CGH. As a result, the method of this thesis was able to obtain an excellent reconstructed image compared with the case to derive the error diffusion coefficient from only the position of the input image.

• Journal of Information Science Theory and Practice
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• v.1 no.2
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• pp.84-92
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• 2013

In this study, the properties of the object types required to describe the relationship among research data resources, which may be generated during the life cycle of the research, are derived. The properties of Fedora Commons and DSpace, which are open source software used for resource management, and schema properties published in DataCite were analyzed. Based on relation names of Fedora Commons, nine new relation names were derived. Thirty-eight object type properties consolidating the target properties of the analysis were derived. The result of this study can be used as basic material for crosswalk research studies of object type relation terms to ensure interoperability among the systems.