• Journal of Information Processing Systems
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• v.16 no.6
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• pp.1250-1260
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• 2020

This study proposes a novel deep neural network model that can accurately detect objects and their relationships in an image and represent them as a scene graph. The proposed model utilizes several multimodal features, including linguistic features and visual context features, to accurately detect objects and relationships. In addition, in the proposed model, context features are embedded using graph neural networks to depict the dependencies between two related objects in the context feature vector. This study demonstrates the effectiveness of the proposed model through comparative experiments using the Visual Genome benchmark dataset.

• Ocean Systems Engineering
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• v.10 no.4
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• pp.415-434
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• 2020

Detecting objects is important for the safe operation of ships, and enables collision avoidance, risk detection, and autonomous sailing. This study proposes a ship detection method from images and videos taken at sea using one of the state-of-the-art deep neural network-based object detection algorithms. A deep learning model is trained using a public maritime dataset, and results show it can detect all types of floating objects and classify them into ten specific classes that include a ship, speedboat, and buoy. The proposed deep learning model is compared to a universal trained model that detects and classifies objects into general classes, such as a person, dog, car, and boat, and results show that the proposed model outperforms the other in the detection of maritime objects. Different deep neural network structures are then compared to obtain the best detection performance. The proposed model also shows a real-time detection speed of approximately 30 frames per second. Hence, it is expected that the proposed model can be used to detect maritime objects and reduce risks while at sea.

• Journal of Sensor Science and Technology
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• v.33 no.2
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• pp.78-85
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• 2024

We proposed a method for visual place recognition that represents images using objects as visual words. Visual words represent the various objects present in urban environments. To detect various objects within the images, we implemented and used a zero-shot detector based on a large-scale image language model. This zero-shot detector enables the detection of various objects in urban environments without additional training. In the process of creating histograms using the proposed method, frequency-based weighting was applied to consider the importance of each object. Through experiments with open datasets, the potential of the proposed method was demonstrated by comparing it with another method, even in situations involving environmental or viewpoint changes.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.5
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• pp.73-82
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• 1996

In this paper, an efficient algorithm for recognizing and positioning occuluded objects in a two-dimensional plane is presented. Model objects and unknown input image are approximated by polygonal boundaries, which are compactly represented by shape functions of the polygons. The input image is partitioned into measningful segments whose end points are at the locations of possible occlusion - i.e. at concave vertices. Each segment is matched against known model objects by calculating a matching measure, which is defined as the minimum euclidean distance between the shape functions. An O(mm(n+m) algorithm for computing the measure is presentd, where n and m are the number of veritces for a model and an unknown object, respectively. Match results from aprtial segments are combined based on mutual compatibility, then are verified using distance transformation and translation vector to produce the final recognition. The proposed algorithm is invariant under translation and rotation of objects, which has been shown by experimental results.

• Journal of the Korea Society for Simulation
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• v.9 no.4
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• pp.25-40
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• 2000

The cost of simulation modeling, the expertise required, and the pains of starting a new each time are impediments to more wide spread adoption of simulation technology. In addition, one of the most critical problems in the field of computer simulation today is the lack of published models and physical objects within the World Wide Web (WWW) allowing such distribution. From the viewpoint of WWW as distributed model repositories, it can be assumed that very many simulation models exist on the web. This paper is based on the premise that WWW is a distributed repository. Design Pattern, web-oriented technology like Java and CORBA, which are especially to cope with distributed objects, are introduced and discussed in detail for integration of simulation model. In this paper an architecture of model integration is proposed, which presents the whole procedure of model integration and how the Internet technologies are connected in. The central focus of this research is on the technical realization of integrating simulation models as distributed objects

• Proceedings of the Korea Society for Simulation Conference
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• 1999.10a
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• pp.325-330
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• 1999

Web-based simulation is one of the most interesting field of simulation research today. Among many research area of web-based simulation, we concern about what a effective way of building simulation model is since creating comprehensive simulation models can be expensive and time consuming. So this paper discusses how to integrate distributed simulation sub-models as objects for constructing the required simulation model which is more large and complex. We introduce two web-oriented methodologies (such as JIDL, CORBA) and the concepts of agent for assisting modelers to integrate simulation models scattered over the web. SINDBAD, which we designed, is a simulation environment which makes it possible constructing a simulation model with distributed model objects on the web and performing the parallel simulation in a distributed way. It is organized according to design patterns in the object oriented concept. Actually we are on the premise that all the distributed objects are originally composed in a CORBA-compatible way to start with our prototype of SINDBAD.

• The KIPS Transactions:PartD
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• v.10D no.6
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• pp.873-884
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• 2003

So far, significant achievements have been studied on the development of models for spatial and spatiotemporal objects with indeterminate boundaries which are found in many applications for geographic analysis and image understanding. Therefore, in this paper we propose the spatiotemporal data model which is applicable for spatial and spatiotemporal objects with uncertainty. Based on this model, we defined topological relationships among the indeterminate spatiotemporal objects and designed the algorithm for the operations. For compatibility with existing spatial models, the proposed model has been designed by extending the spatiotemporal object model which is based on the open GIS specification. We defined indeterminate spatial objects, such as the objects whose position and the shape change discretely over time, and the objects whose shape changes continuously as well as the position. We defined topological relationships among these objects using the extended 9-IM. The proposed model can be efficiently applied to the management systems of natural resource data, westher information, geographic information. and so on.

• Journal of Information Technology Services
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• v.12 no.3
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• pp.311-321
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• 2013

In the community of software engineering, diverse modeling languages are used for representing all relevant information in the form of models. Also many different models such as business model, business process model, product models, interface models etc. are generated through software life cycles. In this situation, models need to be integrated for enterprise integration and enhancement of software productivity. Researchers propose rebuilding models by a specific modeling language, using a intemediate modeling language and using common reference for model integration. However, in the current approach it requires a lot of cost and time to integrate models. Also it is difficult to identify common objects from several models and to update objects in the repository of common model objects. This paper proposes software model integration using metadata model based on Linked data. We verify the effectiveness of the proposed approach through a case study.

• Proceedings of the IEEK Conference
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• 2004.08c
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• pp.823-826
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• 2004

In this paper, A tracking of the moving objects using normalized hue distribution in HSI color model was proposed. Moving objects are detected by using difference image method and integral projection method to background image and objects image only with hue area. Hue information of the detected moving area are normalized by 24 levels from $0^{\circ}$ to $3600^{\circ}A$ distance in between normalized levels with a hue distribution chart of the normalized moving objects is used for the identity distinction feature parameters of the moving objects. To examine proposed method in this paper, image of moving cars are obtained by setting up three cameras at different places every 1 km on outer motorway. The simulation results of identity distinction show that it is possible to distinct the identity a distance in between normalization levels of a hue distribution chart without background.

• The Journal of Information Technology
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• v.10 no.4
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• pp.27-39
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• 2007

Object tracking in a real time environment is one of challenging subjects in computer vision area during past couple of years. This paper proposes a method of object detection and tracking using adaptive background estimation in real time environment. To obtain a stable and adaptive background, we combine 3-frame differential method and running average single gaussian background model. Using this background model, we can successfully detect moving objects while minimizing false moving objects caused by noise. In the tracking phase, we propose a matching criteria where the weight of position and inner brightness distribution can be controlled by the size of objects. Also, we adopt a Kalman Filter to overcome the occlusion of tracked objects. By experiments, we can successfully detect and track objects in real time environment.