• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.10
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• pp.5039-5055
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• 2018

The quality of natural outdoor images captured by visible camera sensors is usually degraded by the haze present in the atmosphere. In this paper, a fast image dehazing method based on visible image and near-infrared fusion is proposed. In the proposed method, a visible and a near-infrared (NIR) image of the same scene is fused based on the dual-tree complex wavelet transform (DT-CWT) to generate a dehazed color image. The color of the fusion image is regulated through haze concentration estimated by dark channel prior (DCP). The experiment results demonstrate that the proposed method outperforms the conventional dehazing methods and effectively solves the color distortion problem in the dehazing process.

• Science of Emotion and Sensibility
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• v.24 no.4
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• pp.53-68
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• 2021

The continuous development of in-vehicle information systems in recent years has dramatically enriched drivers' driving experience while occupying their cognitive resources to varying degrees, causing driving distraction. Under this complex information system, managing the complexity and priority of information and further improvement in driving safety has become a key issue that needs to be urgently solved by the in-vehicle information system. The new interactive methods incorporating the augmented reality (AR) and head-up display (HUD) technologies into in-vehicle information systems are currently receiving widespread attention. This superimposes various onboard information into an actual driving scene, thereby meeting the needs of complex tasks and improving driving safety. Based on the qualitative research methods of surveys and telephone interviews, this study collects the information needs of the target user groups (i.e., beginners and skilled drivers) and constructs a three-mode information database to provide the basis for a customized AR-HUD interface design.

• 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.

• International Journal of Contents
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• v.6 no.2
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• pp.1-5
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• 2010

The rapid growth in communication technology has led to the development of effective ways of sharing ideas and information in the form of speech and images. Understanding this information has become an important research issue and drawn the attention of many researchers. Text in a digital image contains much important information regarding the scene. Detecting and extracting this text is a difficult task and has many challenging issues. The main challenges in extracting text from natural scene images are the variation in the font size, alignment of text, font colors, illumination changes, and reflections in the images. In this paper, we propose a connected component based method to automatically detect the text region in natural images. Since text regions in mages contain mostly repetitions of vertical strokes, we try to find a pattern of closely packed vertical edges. Once the group of edges is found, the neighboring vertical edges are connected to each other. Connected regions whose geometric features lie outside of the valid specifications are considered as outliers and eliminated. The proposed method is more effective than the existing methods for slanted or curved characters. The experimental results are given for the validation of our approach.

• The Journal of Korea Robotics Society
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• v.7 no.2
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• pp.92-100
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• 2012

A map of complex environment can be generated using a robot carrying sensors. However, representation of environments directly using the integration of sensor data tells only spatial existence. In order to execute high-level applications, robots need semantic knowledge of the environments. This research investigates the design of a system for recognizing objects in 3D point clouds of urban environments. The proposed system is decomposed into five steps: sequential LIDAR scan, point classification, ground detection and elimination, segmentation, and object classification. This method could classify the various objects in urban environment, such as cars, trees, buildings, posts, etc. The simple methods minimizing time-consuming process are developed to guarantee real-time performance and to perform data classification on-the-fly as data is being acquired. To evaluate performance of the proposed methods, computation time and recognition rate are analyzed. Experimental results demonstrate that the proposed algorithm has efficiency in fast understanding the semantic knowledge of a dynamic urban environment.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.4
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• pp.674-679
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• 2006

In this paper a gesture recognition system using 3D feature data is described. The system relies on a novel 3D sensor that generates a dense range mage of the scene. The main novelty of the proposed system, with respect to other 3D gesture recognition techniques, is the capability for robust recognition of complex hand postures such as those encountered in sign language alphabets. This is achieved by explicitly employing 3D hand features. Moreover, the proposed approach does not rely on colour information, and guarantees robust segmentation of the hand under various illumination conditions, and content of the scene. Several novel 3D image analysis algorithms are presented covering the complete processing chain: 3D image acquisition, arm segmentation, hand -forearm segmentation, hand pose estimation, 3D feature extraction, and gesture classification. The proposed system is tested in an application scenario involving the recognition of sign-language postures.

• Journal of the Ergonomics Society of Korea
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• v.31 no.4
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• pp.567-575
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• 2012

Objective: The aim of this study is to get an optimal mental model by investigating user's natural behavior for controlling smart TV by mid-air gestures and to identify which factor is most important for controlling behavior. Background: A lot of TV companies are trying to find simple controlling method for complex smart TV. Although plenty of gesture studies proposing they could get possible alternatives to resolve this pain-point, however, there is no fitted gesture work for smart TV market. So it is needed to find optimal gestures for it. Method: (1) Eliciting core control scene by in-house study. (2) Observe and analyse 20 users' natural behavior as types of hand-held devices and control scene. We also made taxonomies for gestures. Results: Users' are trying to do more manipulative gestures than symbolic gestures when they try to continuous control. Conclusion: The most natural way to control smart TV on the remote with gestures is give user a mental model grabbing and manipulating virtual objects in the mid-air. Application: The results of this work might help to make gesture interaction guidelines for smart TV.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.912-928
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• 2019

Crowd behavior analysis research has revealed a central role in helping people to find safety hazards or crime optimistic forecast. Thus, it is significant in the future video surveillance systems. Recently, the growing demand for safety monitoring has changed the awareness of video surveillance studies from analysis of individuals behavior to group behavior. Group detection is the process before crowd behavior analysis, which separates scene of individuals in a crowd into respective groups by understanding their complex relations. Most existing studies on group detection are scene-specific. Crowds with various densities, structures, and occlusion of each other are the challenges for group detection in diverse crowded scenes. Therefore, we propose a group detection approach called Collective Interaction Filtering to discover people motion interaction from trajectories. This approach is able to deduce people interaction with the Expectation-Maximization algorithm. The Collective Interaction Filtering approach accurately identifies groups by clustering trajectories in crowds with various densities, structures and occlusion of each other. It also tackles grouping consistency between frames. Experiments on the CUHK Crowd Dataset demonstrate that approach used in this study achieves better than previous methods which leads to latest results.

• ETRI Journal
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• v.44 no.1
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• pp.51-61
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• 2022

In this paper, we introduced a novel voxel-wise UV parameterization and view-dependent texture synthesis for the immersive rendering of a truncated signed distance field (TSDF) scene model. The proposed UV parameterization delegates a precomputed UV map to each voxel using the UV map lookup table and consequently, enabling efficient and high-quality texture mapping without a complex process. By leveraging the convenient UV parameterization, our view-dependent texture synthesis method extracts a set of local texture maps for each voxel from the multiview color images and separates them into a single view-independent diffuse map and a set of weight coefficients for an orthogonal specular map basis. Furthermore, the view-dependent specular maps for an arbitrary view are estimated by combining the specular weights of each source view using the location of the arbitrary and source viewpoints to generate the view-dependent textures for arbitrary views. The experimental results demonstrate that the proposed method effectively synthesizes texture for an arbitrary view, thereby enabling the visualization of view-dependent effects, such as specularity and mirror reflection.

• Journal of English Language & Literature
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• v.57 no.5
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• pp.871-887
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• 2011

In Lacanian terms, the real, which is a non-representative Ding an sich, is indirectly approachable only in and through language. This 'speaking of the real' is made possible through a restoration of the missing link between one signifier, S1 and another signifier, S₂, as is manifested in the Lacanian formula of metaphor. In Freudian terms of textual metaphor, the missing link is restored by substituting a new edition for an old edition of one's historical text of life. This is what this essay means by the metaphorical/dualistic structure of the analytic/literary text. And this is a way of talking about an intertextuality between literature and psychoanalysis in the sense of the 'text as psyche' and the 'psyche as text.' Applying the 'signifying substitution' to the Oedipus complex, the Oedipal child can find a meaning(s), "my erotic indulgement with my Mom is wrong" by metaphorically substituting S₂: the Name of the Father for S1: the Desire of the Mother. This meaning leads to the constitution of the human subject and the formation of the incest taboo, one of the most significant distinctive features of the human being as distinguished from the animals. We can see a similar metaphorical structure of S1-S2 taking place in the literary texts such as Macbeth and "Dover Beach": in the course of the stage of life being substituted for the primal scene in the former, and the plain of Tucydides for a bed scene in the latter, respectively.