• Journal of KIISE:Computing Practices and Letters
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• v.10 no.2
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• pp.189-198
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• 2004

As display devices evolve, computer-based work environments are also becoming better suited to actual application tasks. This paper discusses the development of an architectural design system using the virtual table, which is a table-type projection system. It consists of the interactive VR modeler, the hybrid tracker and the architectural interpreter. The interactive VR modeler offers visual and tactile guide such as grid interaction, a tangible transparent prop and reference objects, so that a user can design architectural 3D models more easily and intuitively on the virtual table. The hybrid tracker includes two types of tracking methods for viewpoint according to the user's view and hand interaction: namely, vision-based tracking and magnetic tracking. The architectural interpreter automatically transforms simple 3D masses into a basic construction form that has architectural knowledge. The proposed system has advantage in the sense that it is suitable for collaboration among several users, allowing them to view graphical objects in stereoscopic view with direct 3D manipulation. Thus, it can be effectively used for architectural simulation and user-participated design.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.2
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• pp.20-32
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• 2010

Previous hardware devices to capture human motion have many limitations; expensive equipment, complexity of manipulation or constraints of human motion. In order to overcome these problems, real-time motion capture algorithms based on computer vision have been actively proposed. This paper presents an efficient analysis method of multiple view images for real-time motion capture. First, we detect the skin color regions of human being, and then correct the image coordinates of the regions by using camera calibration and epipolar geometry. Finally, we track the human body part and capture human motion using kalman filter. Experimental results show that the proposed algorithm can estimate a precise position of the human body.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.6
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• pp.44-56
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• 2008

Previous hardware devices to capture human motion have many limitations; expensive equipment, complexity of manipulation or constraints of human motion. In order to overcome these problems, real-time motion capture algorithms based on computer vision have been actively proposed. This paper presents an efficient analysis method of multiple view images for real-time motion capture. First, we detect the skin color regions of human being, and then correct the image coordinates of the regions by using camera calibration and epipolar geometry. Finally, we track the human body part and capture human motion using kalman filter. Experimental results show that the proposed algorithm can estimate a precise position of the human body.

• Journal of English Language & Literature
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• v.55 no.6
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• pp.1163-1191
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• 2009

As many critics do, this article starts from the premise that Charles Waddell Chesnutt wrote The Conjure Woman with a distinct socio-ethical view to ameliorating white readers' racism. For this purpose of social activism, first, the author uses a racially submissive genre and narrator- antebellum plantation-dialect fiction and an old ex-slave Julius-in order to win the attention of white racists, who constituted the majority of the reading public of postbellum America. Chesnutt then allows this seemingly submissive ex-slave consecutively to wage narrative battles against a Northern white capitalist, John. This fiction's structure is thus based on interracial narrative conflict. Granted, the result of these narrative battles is Julius's defeat. Even though he sometimes has narrative success through his manipulation of either his white female auditor's sentimentalism or the white capitalist's racial prejudice, it does not lead to any fundamental change in the white audience members' awareness: John still regards Julius's tacitly reformoriented tales merely as nonsensical ghost stories invented by the absurd imagination of a subservient, entertaining, and exploitable black coachman. Admitting his defeat, Julius relinquishes his original goal of deterring John's capitalist exploitation of both racial Others and the natural environment of the South and finally decides to serve the economic power of white capitalism. This self-defeating conclusion, however, should not be identified with Chesnutt's failure as an author. Rather, it should be understood as an interim result of the black author's earnest experiment with literary media best suited to his reform project. In fact, this narrative failure reveals Chesnutt's accurate diagnosis of the postbellum literary world: a black voice is still feebly heard and even easily buried by the whites' capitalist ambition and consequently intensifying racism. Conclusively, Julius's narrative failure should be positively evaluated as Chesnutt's one step further in his gradual and lifelong progress to a narrative goopher effectively to engage whites' imagination and sympathy for a vision of equal interracial coexistence.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.12
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• pp.509-516
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• 2022

Accurate pose prediction of objects in 3D space is an important visual recognition technique widely used in many applications such as scene understanding in both indoor and outdoor environments, robotic object manipulation, autonomous driving, and augmented reality. Most previous works for object pose estimation have the limitation that they require an exact 3D CAD model for each object. Unlike such previous works, this paper proposes a novel neural network model that can predict the poses of unknown objects based on only their RGB color images without the corresponding 3D CAD models. The proposed model can obtain depth maps required for unknown object pose prediction by using an adaptive depth estimator, AdaBins,. In this paper, we evaluate the usefulness and the performance of the proposed model through experiments using benchmark datasets.

• International Journal of Computer Science & Network Security
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• v.23 no.6
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• pp.17-26
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• 2023

Blockchain technology has emerged as one of the most crucial solutions in numerous industries, including healthcare. The combination of blockchain technology and cloud computing results in improving access to high-quality telemedicine and healthcare services. In addition to developments in healthcare, the operational strategy outlined in Vision 2030 is extremely essential to the improvement of the standard of healthcare in Saudi Arabia. The purpose of this survey is to give a thorough analysis of the current state of healthcare technologies that are based on blockchain and cloud computing. We highlight some of the unanswered research questions in this rapidly expanding area and provide some context for them. Furthermore, we demonstrate how blockchain technology can completely alter the medical field and keep health records private; how medical jobs can detect the most critical, dangerous errors with blockchain industries. As it contributes to develop concerns about data manipulation and allows for a new kind of secure data storage pattern to be implemented in healthcare especially in telemedicine fields is discussed diagrammatically.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.5
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• pp.57-66
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• 2022

The Road Management Administration is cracking down on overloaded vehicles by installing low-speed or high-speed WIMs at toll gates and main lines on expressways. However, in recent years, the act of intelligently evading the overloaded-vehicle control system of the Road Management Administration by illegally manipulating the variable axle of an overloaded truck is increasing. In this manipulation, when entering the overloaded-vehicle checkpoint, all axles of the vehicle are lowered to pass normally, and when driving on the main road, the variable axle of the vehicle is illegally lifted with the axle load exceeding 10 tons alarmingly. Therefore, this study developed a technology to detect the state of the variable axle of a truck driving on the road using roadside camera images. In particular, this technology formed the basis for cracking down on overloaded vehicles by lifting the variable axle after entering the checkpoint and linking the vehicle with the account information of the checkpoint. Fundamentally, in this study, the tires of the vehicle were recognized using the Mask RCNN algorithm, the recognized tires were virtually arranged before and after the checkpoint, and the height difference of the vehicle was measured from the arrangement to determine whether the variable axle was lifted after the vehicle left the checkpoint.

• Journal of Broadcast Engineering
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• v.21 no.3
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• pp.425-435
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• 2016

In this paper, we propose a classification model by analyzing raw material images recorded using a color CCD camera to automatically classify good and defective agricultural products such as rice, coffee, and green tea, and raw materials. The current classifying agricultural products mainly depends on visual selection by skilled laborers. However, classification ability may drop owing to repeated labor for a long period of time. To resolve the problems of existing human dependant commercial products, we propose a vision based automatic raw material classification combining mean shift clustering and stepwise region merging algorithm. In this paper, the image is divided into N cluster regions by applying the mean-shift clustering algorithm to the foreground map image. Second, the representative regions among the N cluster regions are selected and stepwise region-merging method is applied to integrate similar cluster regions by comparing both color and positional proximity to neighboring regions. The merged raw material objects thereby are expressed in a 2D color distribution of RG, GB, and BR. Third, a threshold is used to detect good and defective products based on color distribution ellipse for merged material objects. From the results of carrying out an experiment with diverse raw material images using the proposed method, less artificial manipulation by the user is required compared to existing clustering and commercial methods, and classification accuracy on raw materials is improved.