• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.10A
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• pp.990-999
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• 2002

The object-oriented(OO) distributed real-time(RT) programming movement started in 1990's and is growing rapidly at this turn of the century. Distributed real-time simulation is a field in its infancy but it is bounded to receive steadily growing recognition for its importance and wide applicability. The scheme is called the distributed time-triggered simulation scheme which is conceptually simple and easy to use but widely applicable. A new generation object oriented(OO) RT programming scheme is called the time-triggered message triggered object(TMO) programming scheme and it is used to make specific illustrations of the issues. The TMO structuring scheme is a general-style components structuring scheme and supports design of all types of component including hard real time objects and non real time objects within one general structure.

• Journal of the Semiconductor & Display Technology
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• v.12 no.1
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• pp.1-7
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• 2013

Grey-level statistical models have been widely used in many applications for object location and identification. However, conventional models yield some problems in model refinement when training images are not properly aligned, and have difficulties for real-time recognition of arbitrarily rotated models. This paper presents improved grey-level statistical models that align training images using image or feature matching to overcome problems in model refinement of conventional models, and that enable real-time recognition of arbitrarily rotated objects using efficient hierarchical search methods. Edges or features extracted from a mean training image are used for accurate alignment of models in the search image. On the aligned position and orientation, fitness measure based on grey-level statistical models is computed for object recognition. It is demonstrated in various experiments in PCB inspection that proposed methods are superior to conventional methods in recognition accuracy and speed.

• Journal of the Optical Society of Korea
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• v.15 no.3
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• pp.264-271
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• 2011

In this paper we examine and compare the computational speeds of three-dimensional (3D) object recognition by use of digital holography based on central unit processing (CPU) and graphic processing unit (GPU) computing. The holographic fringe pattern of a 3D object is obtained using an in-line interferometry setup. The Fourier matched filters are applied to the complex image reconstructed from the holographic fringe pattern using a GPU chip for real-time 3D object recognition. It is shown that the computational speed of the 3D object recognition using GPU computing is significantly faster than that of the CPU computing. To the best of our knowledge, this is the first report on comparisons of the calculation time of the 3D object recognition based on the digital holography with CPU vs GPU computing.

• Proceedings of the IEEK Conference
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• 2002.07c
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• pp.1843-1846
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• 2002

The object-oriented(OO) distributed real- time(RT) programming movement started in 1990’s and is growing rapidly at this turn of the century Distributed real-time simulation is a field in its infancy but it is bounded to receive steadily growing recognition for its importance and wide applicability. The scheme is called the distributed time-triggered simulation scheme which is conceptually simple and easy to use but widely applicable. A new generation object oriented(00) RT programming scheme is called the time-triggered message triggered object(TMO) programming scheme and it is used to make specific illustrations of the issues. The TMO structuring scheme is a general-style components structuring scheme and supports design of all types of component including hard real time 1 objects and non real time objects within one general structure.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.3
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• pp.368-373
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• 2007

In recent year s there have been increasing interests in real-time object tracking with image information. This dissertation presents a real-time object tracking method through the object recognition based on neural networks that have robust characteristics under various illuminations. This dissertation proposes a global search and a local search method to track the object in real-time. The global search recognizes a target object among the candidate objects through the entire image search, and the local search recognizes and track only the target object through the block search. This dissertation uses the object color and feature information to achieve fast object recognition. The experiment result shows the usefulness of the proposed method is verified.

• International Journal of Advanced Culture Technology
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• v.7 no.3
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• pp.134-142
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• 2019

As the 4th industry has been widely distributed, there is a need for a process of real-time image recognition in various fields such as identification of company employees, security maintenance, and development of military weapons. Therefore, in this paper, we will propose an algorithm that effectively recognizes a test object by comparing it with the DB model. The proposed object recognition system first expresses the outline of the test object as a set of vertices with the distances of predefined length or more. Then, the degree of matching of the structures of the two objects is calculated by examining the distances to the outline of the DB model from the vertices constituting the test object. Because the proposed recognition algorithm uses the outline of the object, the recognition process is easy to understand, simple to implement, and a satisfactory recognition result is obtained.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.1
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• pp.158-164
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• 2009

The real time security system using web camera shall correspond in commensurate with it in real time through classifying moving object and analyzing the behavior. But, as to the detection of moving object in real time image through a camera, it is difficult to detect movement correctly according to the change of unnecessary noises, lighting conditions and screened phenomenon. This paper proposes real time security system by dual camera and ultrasonic sensor, a method of advanced detection in order to detect correct movement of specific object. That is, we could improve the tracing characteristics by using ultrasonic sensor as measurement factor of changed position and verify through experiments that the information interchanged between camera upwards and in front of it have effect on tracing a specific object continuously. The results of the experiment show that recognition rate of object was 97.4% and the correct tracing could be done lastingly in a phenomena of screening object.

• 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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.7C
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• pp.421-427
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• 2011

The importance of the real-time object recognition and tracking field has been growing steadily due to rapid advancement in the computer vision applications industry. As is well known, the mean-shift algorithm is widely used in robust real-time object tracking systems. Since the mentioned algorithm is easy to implement and efficient in object tracking computation, many say it is suitable to be applied to real-time object tracking systems. However, one of the major drawbacks of this algorithm is that it always converges to a local mode, failing to perform well in a cluttered environment. In this paper, an Optical Flow-based algorithm which fits for real-time recognition of multiple moving objects is proposed. Also in the tests, the newly proposed method contributed to raising the similarity of multiple moving objects, the similarity was as high as 0.96, up 13.4% over that of the mean-shift algorithm. Meanwhile, the level of pixel errors from using the new method keenly decreased by more than 50% over that from applying the mean-shift algorithm. If the data processing speed in the video surveillance systems can be reduced further, owing to improved algorithms for faster moving object recognition and tracking functions, we will be able to expect much more efficient intelligent systems in this industrial arena.

• 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.