• Journal of the Korea Society of Computer and Information
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• v.6 no.4
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• pp.104-109
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• 2001

In this paper, we designed a 3D shape measuring system with high speed and high measurement resolution using line-shaped sine stripes of a LCD projector We proposed an effective method to improve measurement efficiency for a 3D shape measuring system by finding the deficient shape information areas and recovering the shape information efficiently. We experimentally confirmed the improvement of measurement efficiency. Deficient shape information areas can be inevitably existed in a acquired image caused by the camera view angle and surface shapes of an object. The measurement efficiency is turned out to be improved by extracting these shadow areas and recovering the shape information efficiently using both a variable rated normalization and a variable sized phase recovering windows.

• Journal of Integrative Natural Science
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• v.12 no.2
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• pp.35-43
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• 2019

In the content-based image search properties, form information is simple because only the contours of objects are available, and although it can effectively extract the characteristics of the objects, it is sensitive to external noise. The radial distortion, one of these noises, is most prominent in the eyewear and, due to the structural characteristics of the imaging equipment, radiative distortion occurs in almost all imaging equipment. It is very important to determine the similarity of the objects in the images in which these distortions occurred to the actual objects. In order to improve this problem, we propose a strong image search technique for formative noise and radiative distortion using regularization phase angles and moments. Through simulation using Wang DB, the proposed algorithm proved excellent performance for radiation distortion that occurs in general. In addition, a system optimized for database can be implemented by making appropriate changes to the threshold values, enabling image retrieval with the desired level of confidence in various systems. The algorithm proposed in this paper is expected to be utilized as an optimal imaging system by extracting morphological form information of multimedia data.

• Journal of the Semiconductor & Display Technology
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• v.16 no.3
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• pp.111-115
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• 2017

Terahertz waves are superior to conventional X-ray or Magnetic Resonance Tomography(MRI), and the amount of information that can be transmitted is as large as thousands of times that conventional X-ray or MRI. In addition, Terahertz waves have great performance in analyzing an object which have some layered structure. By using this advantage, we can extract the letters of a page by analyzing information such as absorption amount and reflection amount by irradiating a closed book with pulses of various frequencies within gap of a terahertz wave. However, in the image of each page using the Terahertz wave might be obtained various kinds of noise and the different character occlusion region. So, to extract letters from the terahertz image, we must take the noise and occlusion region away. We have been working to enhancement the image quality in various ways, and keep on studying de-noising processing for enhancement about the image quality and high resolution. Finally, we also keep on studying about OCR(Optical Character Recognition) technology, which based on pattern matching technique, to read letters.

• The Korean Journal of Applied Statistics
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• v.32 no.1
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• pp.15-27
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• 2019

The reliability of the data means the overall consistency of the measurement results. Methods for evaluating reliability have been continuously studied because it affects the results of the statistical analysis. Cronbach's ${\alpha}$ coefficient or intraclass correlation coefficient is used for reliability evaluation; however, these measures cannot be applied to data consisting of three-dimensional coordinates to represent an object. In this study, we propose a measure to evaluate the reliability of three dimensional shapes based on statistical shape analysis that is made possible by extracting the shape information from the measured three dimensional coordinates and decomposing the shape variation.

• Korean Journal of Remote Sensing
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• v.37 no.1
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• pp.111-122
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• 2021

Building footprint extraction is an active topic in the domain of remote sensing, since buildings are a fundamental unit of urban areas. Deep convolutional neural networks successfully perform footprint extraction from optical satellite images. However, semantic segmentation produces coarse results in the output, such as blurred and rounded boundaries, which are caused by the use of convolutional layers with large receptive fields and pooling layers. The objective of this study is to generate visually enhanced building objects by directly extracting the vertices of individual buildings by combining instance segmentation and keypoint detection. The target keypoints in building extraction are defined as points of interest based on the local image gradient direction, that is, the vertices of a building polygon. The proposed framework follows a two-stage, top-down approach that is divided into object detection and keypoint estimation. Keypoints between instances are distinguished by merging the rough segmentation masks and the local features of regions of interest. A building polygon is created by grouping the predicted keypoints through a simple geometric method. Our model achieved an F1-score of 0.650 with an mIoU of 62.6 for building footprint extraction using the OpenCitesAI dataset. The results demonstrated that the proposed framework using keypoint estimation exhibited better segmentation performance when compared with Mask R-CNN in terms of both qualitative and quantitative results.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.689-692
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• 2021

This paper is a prior study to improve the efficiency of offloading based on mobile agents to optimize allocation of computing resources and reduce latency that support user proximity of application services in a Fog/Edge Computing (FEC) environment. We propose an algorithm that effectively reduces the execution time and the amount of memory required when extracting optimal moving patterns from the vast set of spatio-temporal movement history data of moving objects. The proposed algorithm can be useful for the distribution and deployment of computing resources for computation offloading in future FEC environments through frequency-based optimal path extraction.

• Journal of KIISE:Computing Practices and Letters
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• v.7 no.5
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• pp.498-509
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• 2001

Collaborative systems allow users, who may be far removed from each other geographically, to do collaborative work such as 3D animation, computer game, and industrial design in a single virtual space. This paper describes our experience to develop a collaborative system framework that aims at expanding the some functions of a stand-alone visual modeling tool, called 3D Studio Max, into those of the distributed collaborative working environments. The paper mainly deals with design and implementation of a 3D shared-object Plug-In with respect to the 3D Studio Max Plug-In Software Development Kit in the distributed collaborative system developed by the authors. There are two major functions of the proposed scheme; one is to write 3D object-information to the shared memory after extracting it from the 3D Studio Max, the other is to create 3D objects after retrieving them from the shared memory. Also, the proposed scheme provides a simple way of storing 3D objects that have variable size, by means of shared memory which located in between the collaborative system clients and 3D studio Max. One of the remarkable virtures of the Plug-In is to reduce a considerable amount of shared object data which in consequence can mitigate the network overhead. This can be achieved by the fact that the system is able to extract a minimum amount of 3D objects that are required to transmit. Also, using the proposed scheme, user can facilitate 3D Studio Max into distributed collaborative working environments. This, in consequence give many benefits such as saving time as well as eliminating space constraints in the course of 3D modeling when we are under industrial design process.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.309-311
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• 2014

Despite increasing demands for big data application based on the analysis of scattered unstructured data, few relevant studies have been reported. Accordingly, the present study suggests a technique enabling a sentence-based semantic analysis by extracting objects from collected web information and automatically analyzing the relationships between such objects with collective intelligence and language processing technology. To be specific, collected information is stored in DBMS in a structured form, and then morpheme and feature information is analyzed. Obtained morphemes are classified into objects of interest, marginal objects and objects of non-interest. Then, with an inter-object attribute recognition technique, the relationships between objects are analyzed in terms of the degree, scope and nature of such relationships. As a result, the analysis of relevance between the information was based on certain keywords and used an inter-object relationship extraction technique that can determine positivity and negativity. Also, the present study suggested a method to design a system fit for real-time large-capacity processing and applicable to high value-added services.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.39 no.4
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• pp.429-441
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• 2002

This paper concerns extracting 3D motion information from a 3D input device in real time focused to enabling effective human-computer interaction. In particular, we develop a novel algorithm for extracting 6 degrees-of-freedom motion information from a 3D input device by employing an epipolar geometry of stereo camera, color, motion, and structure information, free from requiring the aid of camera calibration object. To extract 3D motion, we first determine the epipolar geometry of stereo camera by computing the perspective projection matrix and perspective distortion matrix. We then incorporate the proposed Motion Adaptive Weighted Unmatched Pixel Count algorithm performing color transformation, unmatched pixel counting, discrete Kalman filtering, and principal component analysis. The extracted 3D motion information can be applied to controlling virtual objects or aiding the navigation device that controls the viewpoint of a user in virtual reality setting. Since the stereo vision-based 3D input device is wireless, it provides users with a means for more natural and efficient interface, thus effectively realizing a feeling of immersion.

• Journal of KIISE:Computer Systems and Theory
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• v.29 no.11
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• pp.589-600
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• 2002

Deformable models offer an attractive approach for extracting three-dimensional boundary structures from volumetric images. However, conventional deformable models have three major limitations - sensitive to initial condition, difficult to represent complex boundaries with severe object concavities and protrusions, and self-intersective between model elements. This paper proposes a deformable model that is effective to extract geometrically complex boundary surfaces by improving away the limitations of conventional deformable models. First, the proposed deformable model resamples its elements hierarchically based on volume image pyramid. The hierarchical resampling overcomes sensitivity to initialization by extracting the boundaries of objects in a multiscale scheme and enhances geometric flexibility to be well adapted to complex image features by refining and regularizing the size of model elements based on voxel size. Second, the physics-based formulation of our model integrates conventional internal and external forces, as well as a non-self-intersecting force. The non-self-intersecting force effectively prevents collision or crossing over between non-neighboring model elements by pushing each other apart if they are closer than a limited distance. We show that the proposed model successively extracts the complex boundaries including severe concavities and protrusions, neither depending on initial position nor causing self-intersection, through the experiments on several computer-generated volume images and brain MR volume images.