• 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%.

• Smart Media Journal
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• v.8 no.2
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• pp.66-73
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• 2019

Recently, object recognition technology using computer vision has attracted attention as a technology to replace sensor-based object recognition technology. It is often difficult to commercialize sensor-based object recognition technology because such approach requires an expensive sensor. On the other hand, object recognition technology using computer vision may replace sensors with inexpensive cameras. Moreover, Real-time recognition is viable due to the growth of CNN, which is actively introduced into other fields such as IoT and autonomous vehicles. Because object recognition model applications demand expert knowledge on deep learning to select and learn the model, such method, however, is challenging for non-experts to use it. Therefore, in this paper, we analyze the structure of deep - learning - based object recognition models, and propose a platform that can automatically select a deep - running object recognition model based on a user 's desired condition. We also present the reason we need to select statistics-based object recognition model through conducted experiments on different models.

• 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 Transactions of the Korea Information Processing Society
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• v.6 no.11S
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• pp.3340-3349
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• 1999

Supporting object group on distributed object system give merits such as load balancing, fault tolerance and high availability. In this paper, we describe a CORBA ORB that has been designed to support object group based on active replication. The ORB supports the operational model in which it uses the IIOP for communication between client and server and total ordered multicast protocol for consistency control among group members. And through extension of ORB, it provides functions required for support of object group. Since it provides transparency of object replication, the ORB is interoperable with the existing CORBA products. It make possible for existing server application to be easily extended to application supporting object group as adding interface functions which should be used for building applications is minimized. A prototype is implemented, and performance of the replicated object group is tested and compared with a single object invocation.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.135-135
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• 2001

In recent years there have been increasing interests in real-time object tracking with image information. Since image information is affected by illumination, this paper presents the real-time object tracking method based on neural networks that have robust characteristics under various illuminations. This paper proposes three steps to track the object and the fast tracking method. In the first step the object color is extracted using neural networks. In the second step we detect the object feature information based on invariant moment. Finally the object is tracked through a shape recognition using neural networks. To achieve the fast tracking performance, we have a global search for entire image and then have tracking the object through local search when the object is recognized.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.23 no.9A
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• pp.2258-2270
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• 1998

Recently, the distributed processing environments provide various open multimedia serivces through telecommunication network and have been developing into information networking structure based on object oriented concepts and distributed systems which can apply new services with a few changes the existing networks. This paper proposes the object group model which is the collection of objects and can functionally and efficiently manage the individual object. this paper presents the analysis of the requirement and the function specifications to propose the object group model, and depicts the functional structure in details using its analysis. The goal of this paper is to decrease the complexity of the object's management and to voercome the limitations of among the components of object group for management and service functions based on our proposed the object group model and show interaction procedures to eTD (event tracing diagram)s and finally we design the object group model by TINA-ODL.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.12
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• pp.2057-2060
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• 2016

Range-based motion estimation of a moving object by using a camera is proposed. Whereas the existing results constrain the motion of an object for the motion estimation of an object, the constraints on the motion is relieved in the proposed method in that a more generally moving object motion can be handled. To this end, a nonlinear observer is designed based on the relative dynamics between the object and camera so that the object velocity and the unknown camera velocity can be estimated. Stability analysis and simulation results for the moving object are provided to show the effectiveness of the proposed method.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2358-2361
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• 2001

In recent years there have been increasing interests in real-time object tracking with image information. Since image information is affected by illumination, this paper presents the real-time object tracking method based on neural networks which have robust characteristics under various illuminations. This paper proposes three steps to track the object and the fast tracking method. In the first step the object color is extracted using neural networks. In the second step we detect the object feature information based on invariant moment. Finally the object is tracked through a shape recognition using neural networks. To achieve the fast tracking performance, this paper first has a global search of entire image and tracks the object through local search when the object is recognized.

• Journal of Korea Society of Digital Industry and Information Management
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• v.15 no.3
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• pp.15-28
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• 2019

Artificial intelligence is evolving rapidly in the ICT field, smart convergence media system and content industry through the fourth industrial revolution, and it is evolving very rapidly through Big Data. In this paper, we propose a face recognition method based on object recognition based on object recognition through artificial intelligence. In this method, Were experimented and studied through the object recognition technique of artificial intelligence. In the conventional 3D image field, general research on object recognition has been carried out variously, and researches have been conducted on the side effects of visual fatigue and dizziness through 3D image. However, in this study, we tried to solve the problem caused by the quantitative difference between object recognition and object recognition for human factor algorithm that measure visual fatigue through cognitive function, morphological analysis and object recognition. Especially, The new method of computer interaction is presented and the results are shown through experiments.

• Proceedings of the Korea Society of Information Technology Applications Conference
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• 2005.11a
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• pp.31-34
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• 2005

Mobile devices, such as PDAs, portable multimedia players, are more likely to encompass large storage devices with prevalance of high-quality multimedia contents. This paper proposes an object-based multimedia contents storage architecture that employs the object-based storage device model and the iSCSI protocol. It also provides a multimedia content player that operates directly with the proposed storage architecture. We implement both the proposed storage architecture and the multimedia content player upon the Linux environment. Performance evaluation by playing MP3 multimedia contents reveals that the proposed storage architecture reduces the total power consumption by 9%, compared with an existing networked storage. This enhancement is mainly contributed to the fact that a large portion of the file system is moved into the object-based multimedia contents storage from the mobile device.