• Journal of Information Technology Services
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• v.10 no.4
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• pp.269-280
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• 2011

Mobile Augmented Reality (mobile AR), as one of the most prospective mobile applications, intends to provide richer experiences by annotating tags or virtual objects over the scene observed through camera embedded in a handheld device like smartphone or pad. In this paper, we analyzed the current status of the art of mobile AR and proposed a novel Points of Interest (POIs) data model based on ontology to provide context-aware information retrievals on lots of POIs data. Proposed ontology was expanded from the standard POIs data model of W3C POIs Working Group and established using OWL (Web Ontology Language) and Protege. We also proposed a context-aware mobile AR platform which can resolve three distinguished issues in current platforms : interoperability problem of POI tags, POIs data retrieval issue, and context-aware service issue.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.3
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• pp.11-22
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• 2010

This paper is a study on implementation of intelligent image surveillance system using context information and supplements temporal-spatial constraint, the weak point in which it is hard to process it in real time. In this paper, we propose scene analysis algorithm which can be processed in real time in various environments at low resolution video(320*240) comprised of 30 frames per second. The proposed algorithm gets rid of background and meaningless frame among continuous frames. And, this paper uses wavelet transform and edge histogram to detect shot boundary. Next, representative key-frame in shot boundary is selected by key-frame selection parameter and edge histogram, mathematical morphology are used to detect only motion region. We define each four basic contexts in accordance with angles of feature points by applying vertical and horizontal ratio for the motion region of detected object. These are standing, laying, seating and walking. Finally, we carry out scene analysis by defining simple context model composed with general context and emergency context through estimating each context's connection status and configure a system in order to check real time processing possibility. The proposed system shows the performance of 92.5% in terms of recognition rate for a video of low resolution and processing speed is 0.74 second in average per frame, so that we can check real time processing is possible.

• Korean Journal of Cognitive Science
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• v.22 no.2
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• pp.173-192
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• 2011

The visual system applies previously learned contextual knowledge to facilitate the perception and encoding of scenes. When extrapolation following the employment of contextual information occurs, it sometimes leads to scene memory distortion: people report as if they saw more than they actually had seen. This phenomenon is called the "boundary extension" effect (Intraub & Richardson, 1989). The present study aimed to clarify the effects of contextual information on boundary extension in a more systematic way. Based on the assumption that it is harder to extract contextual information from inverted scenes compared to intact scenes, we presented inverted scenes either during encoding or retrieval to manipulate the level of contextual information and compared the magnitude of boundary extension effect for upright versus inverted scenes. In a series of experiments, we found that scene inversion during encoding, but not during retrieval, significantly reduced boundary extension. Showing reduced memory distortion for inverted scenes, the current study directly demonstrated that access to contextual information is a critical component of scene extrapolation process.

• International Journal of Advanced Culture Technology
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• v.9 no.3
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• pp.265-270
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• 2021

VR and AR contents provide a rich user experience [1]. Realistic content with human computer interaction and immersion provides an improved user experience, but there is a limit to producing all elements realistically. In this study, we propose a method to advance the rendering of immersive content using background color information [2]. First, the elements necessary for Kelvin temperature rendering are derived from the color and background as context elements, and the rendering effect has been realized in the soap bubble. For soap bubbles Kelvin temperature rendering, the average color of the background is extracted and the color with the highest similarity is applied by comparing the main color and Kelvin temperature.

• Journal of KIISE:Computing Practices and Letters
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• v.9 no.6
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• pp.712-717
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• 2003

A novel multi-snake is proposed for efficient extraction of multi-object contours from a natural scene. An NTGST(noise-tolerant generalized symmetry transform) is used as a context-free attention operator to detect and locate multiple objects from a complex background and then the snake points are automatically initialized nearby the contour of each detected object using symmetry map of the NTGST before multiple snakes are introduced. These procedures solve the knotty subjects of automatic snake initialization and simultaneous extraction of multi-object contours in conventional snake algorithms. Because the snake points are initialized nearby the actual contour of each object, as close as possible, contours with high convexity and/or concavity can be easily extracted. The experimental results show that the proposed method can efficiently extract multi-object contours from a noisy and complex background of natural scenes.

• The Journal of the Convergence on Culture Technology
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• v.9 no.4
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• pp.423-430
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• 2023

This dissertation discusses the formulation of a nonlinear storytelling board that preserves the contextual perspective of characters. Storytelling encompasses the director's creative intention by leveraging the interaction of various elements to construct a logical narrative that explores cause and effect. Its primary objective is to enhance viewers' empathy. Consequently, there is a pressing need for comprehensive research on differentiating storytelling from storyboarding. Moreover, the integrated approach to storytelling and storyboarding holds scholarly value in understanding the process of narrative composition and visualization. Thus, a study proposes a method for constructing nonlinear storytelling boards considering the discrete camera perspective and contextual scene continuity, ultimately contributing to visual complexity and correlation comprehension. This approach enables a careful and simultaneous consideration of the correlations that deepen cognition, including the physical, emotional, and event rhythms mentioned in Karen Perlman's theory.

• Journal of the Korea Society of Computer and Information
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• v.21 no.1
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• pp.191-198
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• 2016

This paper aims to present how to design and implement a multimedia tutorial system for the self-learning French language using Director with additional tools. To implement a multimedia tutorial system we need to design several steps. First, we should choose the level of the users and design tutorial. Second, we should prepare all materials such as sounds, graphics, text and video. Finally, we should implement the selected elements and control the educational software. Due to the nature of the paper, it must emphasize French basic conversation to make environment that be used in each scene and the scene of the context dialog. In view of the fact that the fitness of each content utilization field of multimedia authoring tool is high, it is possible as part of the system sizing process of the manufacturing process, to impart its meaning. This learning-contents are composed of 10 units each situation, and we anticipate there are the several effects of this system on basic French students. This system helps lecturer get French students interested in lessons, and enables learner to learn French of the role of iterative practice by linking image and sound. Also this system helps learners to prepare and review French studying after a lesson and allows leaners to maximize their efficiency. The future of this work is to implement this system on the app.

• The Journal of the Korea Contents Association
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• v.11 no.8
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• pp.1-13
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• 2011

In order to create an intelligent autonomous agent in virtual world, we need a sophisticated design for perception, recognition, judgement and behavior. We develop the perception and recognition functions for such an autonomous agent. Our perception mechanism identifies lines based on differences in color, the primitive visible data, and exploits those lines to grasp shapes and regions in the scene. We develop an inferencing algorithm that can infer the original shape from a damaged or partially hidden shape using its characteristics from the ontology in order to intelligently recognize the perceived shape. Several individually recognized 2D shapes and their spatial relations form 3D shapes and those 3D shapes in turn constitute a scene. Each 3D shape occupies its respective region, and an agent analyzes the associated objects and relevant scenes to recognize things and phenomena. We also develop a mechanism by which an agent uses this recognition function to accumulate and use her knowledge on the scene in the historical context. We implement these functions presented above against an example situation to demonstrate their sophistication and realism.

• Journal of KIISE:Software and Applications
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• v.34 no.6
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• pp.549-561
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• 2007

Vision-based scene understanding is to infer and interpret the context of a scene based on the evidences by analyzing the images. A probabilistic approach using Bayesian networks is actively researched, which is favorable for modeling and inferencing cause-and-effects. However, it is difficult to gather meaningful evidences sufficiently and design the model by human because the real situations are dynamic and uncertain. In this paper, we propose a learning method of Bayesian network that reduces the computational complexity and enhances the accuracy by searching an efficient BN structure in spite of insufficient evidences and training data. This method represents the domain knowledge as ontology and builds an efficient hierarchical BN structure under constraint rules that come from the ontology. To evaluate the proposed method, we have collected 90 images in nine types of circumstances. The result of experiments indicates that the proposed method shows good performance in the uncertain environment in spite of few evidences and it takes less time to learn.

• Journal of Internet Computing and Services
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• v.15 no.6
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• pp.117-124
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• 2014

Cluttered background is a major obstacle in developing salient object detection and tracking system for mobile device captured natural scene video frames. In this paper we propose a context aware feature vector selection model to provide an efficient noise filtering by machine learning based classifiers. Since the context awareness for feature selection is achieved by searching nearest neighborhoods, known as NP hard problem, we apply a fast approximation method with complexity analysis in details. Separability enhancement in feature vector space by adding the context aware feature subsets is studied rigorously using principal component analysis (PCA). Overall performance enhancement is quantified by the statistical measures in terms of the various machine learning models including MLP, SVM, Naïve Bayesian, CART. Summary of computational costs and performance enhancement is also presented.