• Journal of the Korean School Mathematics Society
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• v.23 no.1
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• pp.129-158
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• 2020

In this study, we presented two geometric tasks to three 11th grade students to identify the characteristics of the images that the students had at the beginning of problem-solving in the problem situations and investigated how their images changed during problem-solving and effected their problem-solving behaviors. In the first task, student A had a static image (type 1) at the beginning of his problem-solving process, but later developed into a dynamic image of type 3 and recognized the invariant relationship between the quantities in the problem situation. Student B and student C were observed as type 3 students throughout their problem-solving process. No differences were found in student B's and student C's images of the problem context in the first task, but apparent differences appeared in the second task. In the second task, both student B and student C demonstrated a dynamic image of the problem context. However, student B did not recognize the invariant relationship between the related quantities. In contrast, student C constructed a robust quantitative structure, which seemed to support him to perceive the invariant relationship. The results of this study also show that the success of solving the task 1 was determined by whether the students had reached the level of theoretical generalization with a dynamic image of the related quantities in the problem situation. In the case of task 2, the level of covariational reasoning with the two varying quantities in the problem situation was brought forth differences between the two students.

• International Journal of Control, Automation, and Systems
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• v.6 no.6
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• pp.828-835
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• 2008

We present a novel method for eye location by means of a two-level classifier scheme. Locating the eye by machine-inspection of an image or video is an important problem for Computer Vision and is of particular value to applications in biomedical imaging. Our method aims to overcome the significant challenge of an eye-location that is able to maintain high accuracy by disregarding highly variable changes in the environment. A first level of computational analysis processes this image context. This is followed by object detection by means of a two-class discrimination classifier(second algorithmic level).We have tested our eye location system using FERET and BioID database. We compare the performance of two-level classifier with that of non-level classifier, and found it's better performance.

• Journal of Electrical Engineering and Technology
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• v.10 no.4
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• pp.1886-1898
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• 2015

In today’s era of advanced technological developments, the threats to the authenticity and integrity of digital images, in a nutshell, the threats to the Image Forensics Research communities have also increased proportionately. This happened as even for the ‘non-expert’ forgers, the availability of image processing tools has become a cakewalk. This image forgery poses a great problem for judicial authorities in any context of trade and commerce. Block matching based image cloning detection system is widely researched over the last 2-3 decades but this was discouraged by higher computational complexity and more time requirement at the algorithm level. Thus, for reducing time need, various dimension reduction techniques have been employed. Since a single technique cannot cope up with all the transformations like addition of noise, blurring, intensity variation, etc. we employ multiple techniques to a single image. In this paper, we have used Fuzzy logic approach for decision making and getting a global response of all the techniques, since their individual outputs depend on various parameters. Experimental results have given enthusiastic elicitations as regards various transformations to the digital image. Hence this paper proposes Fuzzy based cloning detection and classification system. Experimental results have shown that our detection system achieves classification accuracy of 94.12%. Detection accuracy (DAR) while in case of 81×81 sized copied portion the maximum accuracy achieved is 99.17% as regards subjection to transformations like Blurring, Intensity Variation and Gaussian Noise Addition.

• Proceedings of the Korean Institute of Interior Design Conference
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• 2002.04a
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• pp.150-153
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• 2002

There has been many improvements on our nation's public restroom, however, it was focused on interior decoration and installing convenience supplies. The simplicity of materials is another aspect. Besides, the lack of harmony with the surrounding scenery is also a problem. The new concept of public restroom that this study suggests is; a creation of a lively space to stimulate the senses of human and a cultural space that people can rest, and eventually improving the quality of life, being the landmark of streets and parks by avoiding the negative image.

• Journal of the Semiconductor & Display Technology
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• v.7 no.3
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• pp.59-63
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• 2008

Geo-coding refers to the process of associating data with location information, and the system deals with geographic identifiers expressed as latitude and longitude or street addresses. Although many services have been launched, there still remains a problem for users to create geo-coded photo with manually labeling GPS(Global Positioning System) coordinate or synchronizing with separate devices. In this paper, we design and implement a geo-coding system which utilizes the time and location information embedded in digital photographs in order to automatically categorize a personal photo collection. An included GPS receiver labels a photograph with its corresponding GPS coordinates, and the position of the camera is automatically recorded into the photo image header at the moment of capture. The place and time where the photo was taken allows us to provide context metadata on the management and retrieval of information.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.4
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• pp.249-256
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• 2022

In the case of Drone Photogrammetry, the "pixel to point tool" module of Metashape, Pix4D Mapper, ContextCapture, and Global MapperGIS, which is a simple software, are widely used. Each SW has its own logic for the analysis of aerial triangulation, but from the user's point of view, it is necessary to select a SW by comparative analysis of the coordinate values of geospatial information for the result. Taking aerial photos for drone photogrammetry, surveying GCP reference points through VRS-GPS Survey, processing the acquired basic data using each SW to construct ortho image and DSM, and GCPSurvey performance and acquisition from each SW The coordinates (X,Y) of the center point of the GCP target on the Ortho-Image and the height value (EL) of the GCP point by DSM were compared. According to the "Public Surveying Work Regulations", the results of each SW are all within the margin of error. It turned out that there is no problem with the regulations no matter which SW is included within the scope.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.5
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• pp.217-228
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• 2023

Video instance segmentation is an intelligent visual task with high complexity because it not only requires object instance segmentation for each image frame constituting a video, but also requires accurate tracking of instances throughout the frame sequence of the video. In special, human instance segmentation in drama videos has an unique characteristic that requires accurate tracking of several main characters interacting in various places and times. Also, it is also characterized by a kind of the class imbalance problem because there is a significant difference between the frequency of main characters and that of supporting or auxiliary characters in drama videos. In this paper, we introduce a new human instance datatset called MHIS, which is built upon drama videos, Miseang, and then propose a novel video data augmentation method, CDVA, in order to overcome the data imbalance problem between character classes. Different from the previous video data augmentation methods, the proposed CDVA generates more realistic augmented videos by deciding the optimal location within the background clip for a target human instance to be inserted with taking rich spatio-temporal context embedded in videos into account. Therefore, the proposed augmentation method, CDVA, can improve the performance of a deep neural network model for video instance segmentation. Conducting both quantitative and qualitative experiments using the MHIS dataset, we prove the usefulness and effectiveness of the proposed video data augmentation method.

• Journal of IKEEE
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• v.27 no.3
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• pp.251-257
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• 2023

In this paper, we propose a dual-structured self-attention method that improves the lack of regional features of the vision transformer's self-attention. Vision Transformers, which are more computationally efficient than convolutional neural networks in object classification, object segmentation, and video image recognition, lack the ability to extract regional features relatively. To solve this problem, many studies are conducted based on Windows or Shift Windows, but these methods weaken the advantages of self-attention-based transformers by increasing computational complexity using multiple levels of encoders. This paper proposes a dual-structure self-attention using self-attention and neighborhood network to improve locality inductive bias compared to the existing method. The neighborhood network for extracting local context information provides a much simpler computational complexity than the window structure. CIFAR-10 and CIFAR-100 were used to compare the performance of the proposed dual-structure self-attention transformer and the existing transformer, and the experiment showed improvements of 0.63% and 1.57% in Top-1 accuracy, respectively.

• Journal of English Language & Literature
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• v.57 no.1
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• pp.147-170
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• 2011

This paper elucidates the characteristics of transnational adoption, estimates the possibility of beyond-borders identity of transnational adoptees, and tries to analyze Jane Jeong Trenka's The Language of Blood in its context. Though it has been regarded as one of the most humanitarian ways of helping orphans and poor children of the world, transnational adoption, a one-way flow of children from poor Asian countries to rich white countries, has been operated under the market logic between countries. Transnational adoptees, who had been abandoned and forced to be taken away from their birth mother, and later, to fulfill the desire of white parents for a perfect family, perform an ideological labor, serving to make the heterogeneous nuclear family complete. Korean transnational adoptees, forced to transcend the borders of nation, culture, and ethnicity, experience racial conflict and alienation in white adoptive family and society. Their diaspora experience of violent dislocation creates frustration and confusion in establishing their identity as a whole being. When they return to Korea to find their birth mother and their true identity, Korean adoptees, however, are faced with other obstructing issues, such as language problem, culture conflict, and maternal nationalism. Finally, Korean transnational adoptees reject Korean nationalism discourse based on blood, and try to redefine themselves as beyond-borders subjectivities with new and fluid identities. Jane Jeong Trenka's The Language of Blood, an autobiographical novel based on her experiences as a transnational adoptee, represents a Korean adopted girl's personal, cultural, and racial conflict within her white adoptive family, and questions the image of benevolent white mother and the myth of multiculturalism. The novel further represents Jane's return to Korea to find out her true identity, and shows Jane's disappointment and alienation in her birth country due to her ignorance of language and culture. Returning to USA again, and trying to be reconciled with her American mother, Jane shows the promise of accepting her new identity capable of transcending the borders, and thus, the possibility of enlarging the category of belonging.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.6
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• pp.1104-1114
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• 2007

Digital video compression technique has played an important role that enables efficient transmission and storage of multimedia data where bandwidth and storage space are limited. The new video coding standard, H.264/AVC, developed by Joint Video Team(JVT) significantly outperforms previous standards in compression performance. Especially, variable length code(VLC) plays a crucial pun in video and image compression applications. H.264/AVC standard adopted Context-based Adaptive Variable Length Coding(CAVLC) as the entropy coding method. CAVLC of H.264/AVC requires a large number of the memory accesses. This is a serious problem for applications such as DMB and video phone service because of the considerable amount of power that is consumed in accessing the memory. In order to overcome this problem in this paper, we propose a variable length technique that implements memory-free coeff_token, level, and run_before decoding based on arithmetic operations and using only 70% of the required memory at total_zero variable length decoding.