• ETRI Journal
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• v.40 no.2
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• pp.246-256
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• 2018

A spatial augmented reality (SAR) system enables a virtual image to be projected onto the surface of a real-world object and the user to intuitively control the image using a tangible interface. However, occlusions frequently occur, such as a sudden change in the lighting environment or the generation of obstacles. We propose a robust object tracker based on a multithreaded system, which can track an object robustly through occlusions. Our multithreaded tracker is divided into two threads: the detection thread detects distinctive features in a frame-to-frame manner, and the tracking thread tracks features periodically using an optical-flow-based tracking method. Consequently, although the speed of the detection thread is considerably slow, we achieve real-time performance owing to the multithreaded configuration. Moreover, the proposed outlier filtering automatically updates a random sample consensus distance threshold for eliminating outliers according to environmental changes. Experimental results show that our approach tracks an object robustly in real-time in an SAR environment where there are frequent occlusions occurring from augmented projection images.

• Asia pacific journal of information systems
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• v.6 no.1
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• pp.223-240
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• 1996

The paper proposes an approach, called OPNets, for modeling and validating manufacturing systems that is based on object-oriented high-level Petri nets. In OPNets, modeling components of Petri net are constructed into hierarchical objects that communicate with each other by passing messages. To enhance the reusability and maintainability, OPNets organizes a system into hierarchical objects that inherit attributes and behavioral properties from the object of super class and object-interaction relations are separated from the internal structure of object. The modeling scheme of OPNets tries to resolve the complexity problems of Petri net. To illustrate the modeling schemes of OPNets, a storage/retrieval example has been proposed.

• Journal of the Optical Society of Korea
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• v.11 no.3
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• pp.108-112
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• 2007

Spectroscopic ellipsometry (SE) has become an important tool in scatterometry based nano-structure 3D profiling. In this paper, we propose a novel 3D nano object recognition method by use of phase sensitive scatterometry. We claims that only phase sensitive scatterometry can provide a reasonable 3D nano-object recognition capability since phase data gives much higher sensitive 3D information than amplitude data. To show the validity of this approach, first we generate various $0^{th}$ order SE spectrum data ($\psi$ and ${\Delta}$) which can be calculated through rigorous coupled-wave analysis (RCWA) algorithm and then we calculate correlation values between a reference spectrum and an object spectrum which is varied for several different object 3D shape.

• Journal of the Korea Institute of Military Science and Technology
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• v.25 no.4
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• pp.381-389
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• 2022

Detecting partially occluded objects is difficult due to the appearances and shapes of occluders are highly variable. These variabilities lead to challenges of localizing accurate bounding box or classifying objects with visible object parts. To address these problems, we propose a two-stage part-based attention approach for robust object detection under partial occlusion. First, our part attention network(PAN) captures the important object parts and then it is used to generate weighted object features. Based on the weighted features, the re-weighted object features are produced by our reinforced PAN(RPAN). Experiments are performed on our collected military vehicle dataset and synthetic occlusion dataset. Our method outperforms the baselines and demonstrates the robustness of detecting objects under partial occlusion.

• Journal of KIISE:Computing Practices and Letters
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• v.7 no.2
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• pp.141-150
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• 2001

Manufacturing conb'ol applications consist of concurrent active components such as robots, AGV's (Automatic Guided Vehicles), and conveyors. Running of manufacturing control programs is interactions among those components. We can enhance the productivity and extendability of manufacturing control applications by using the object-oriented teclmology that models those components as reusable objects. But the objects in current object-oriented technology that encapsulate state and behavior infonnation are passive in a sense that those respond only when messages are sent to them. In this paper, we introduce the Distributed Active Object Systems (DAGS) approach that SUPPOltS active objects. Since active objects encapsulate control infonnation in addition to state and behavior information under COREA/Java-based distributed environment, they can represent manufacturing control components better than the objects in ordimuy object-oriented technology. TIus control infonnation provides an object with a featme that can monitor its own status as well as other object's status connected by intelface valiables. Active objects can initiate a behavior according to the change of those status. Therefore, we can sb-uctmally assemble self-initiating active objects by using intelface variables to construct a system without describing bow to control distributed objects by using message passing. As the DAOS approach supports object composability, we can enhal1ce the productivity and extendability of disbibuted manufactming control applications even better than the ordil1alY object-oriented approach. Also, the DAOS approach supports better component reusability with active objects that encapsulate control information .

• Korean Management Science Review
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• v.12 no.2
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• pp.43-62
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• 1995

For a complex scheduling system like time table construction, its optimal solution, if exists, is hard to obtain. In this paper, the scheduling environment is reasonably confined as where objects have their own events competing for better slots on boards, and objects have their own board slot preferences and belong to one or more classes of the society which globally constrains them. Here, two phase method is suggested, where the first phase is human-like preference driven and the second phase is for fine tuning by considering all the factors given. Designed and implemented in our system HI-SCHED are dynamic object switching, temporal-constraint-driven intelligent backtracking, case-based revisions, object-based approach, and so on. Some satisfaction degrees are also defined to measure the usefulness of our method. In addition, look-ahead dynamic object switching is considered, and additional global constraints are introduced and processed. A simple scheme is also used to verify the usefulness of the post processing scheme.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.230-240
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• 2022

Sharing of online videos via internet is an emerging and important concept in different types of applications like surveillance and video mobile search in different web related applications. So there is need to manage personalized web video retrieval system necessary to explore relevant videos and it helps to peoples who are searching for efficient video relates to specific big data content. To evaluate this process, attributes/features with reduction of dimensionality are computed from videos to explore discriminative aspects of scene in video based on shape, histogram, and texture, annotation of object, co-ordination, color and contour data. Dimensionality reduction is mainly depends on extraction of feature and selection of feature in multi labeled data retrieval from multimedia related data. Many of the researchers are implemented different techniques/approaches to reduce dimensionality based on visual features of video data. But all the techniques have disadvantages and advantages in reduction of dimensionality with advanced features in video retrieval. In this research, we present a Novel Intent based Dimension Reduction Semi-Supervised Learning Approach (NIDRSLA) that examine the reduction of dimensionality with explore exact and fast video retrieval based on different visual features. For dimensionality reduction, NIDRSLA learns the matrix of projection by increasing the dependence between enlarged data and projected space features. Proposed approach also addressed the aforementioned issue (i.e. Segmentation of video with frame selection using low level features and high level features) with efficient object annotation for video representation. Experiments performed on synthetic data set, it demonstrate the efficiency of proposed approach with traditional state-of-the-art video retrieval methodologies.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.7
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• pp.3578-3593
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• 2017

Given two collections of objects that carry both spatial and textual information in the form of tags, a $\text\underline{S}patio$-$\text\underline{T}extual$-based object $\text\underline{S}imilarity$ $\text\underline{JOIN}$ (ST-SJOIN) retrieves the pairs of objects that are textually similar and spatially close. In this paper, we have proposed a block index-based approach called BIST-JOIN to facilitate the efficient ST-SJOIN processing. In this approach, a dual-feature distance plane (DFDP) is first partitioned into some blocks based on four segmentation schemes, and the ST-SJOIN is then transformed into searching the object pairs falling in some affected blocks in the DFDP. Extensive experiments on real and synthetic datasets demonstrate that our proposed join method outperforms the state-of-the-art solutions.

• Transactions of the Korean Society of Mechanical Engineers
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• v.17 no.11
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• pp.2711-2722
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• 1993

Most of the current 2-D object recognition systems are model-based. In such systems, the representation of each of a known set of objects are precompiled and stored in a database of models. Later, they are used to recognize the image of an object in each instance. In this thesis, the approach method for the 2-D object recognition is treating an object boundary as a string of structral units and utilizing string matching to analyze the scenes. To reduce string matching time, models are rebuilt by means of fuzzy c-means clustering algorithm. In this experiments, the image of objects were taken at initial position of a robot from the CCD camera, and the models are consturcted by the proposed algorithm. After that the image of an unknown object is taken by the camera at a random position, and then the unknown object is identified by a comparison between the unknown object and models. Finally, the amount of translation and rotation of object from the initial position is computed.

• 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.