• Journal of Korean Society of Industrial and Systems Engineering
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• v.23 no.60
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• pp.1-9
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• 2000

This paper deals with the difference between human sensibility collected from real product and that through graphic image(photographs and graphic images on CRT monitor) on the same automotive interior. The objective of this study is to verify the possibility that, If there are some restrictions in collecting human sensibility through real product directly, they can be overcome by using graphic image instead, making it easy to collect and analyze human sensibility so as to reflect consumers sensibility in the design of automotive interior, and also comparing the result between real product and graphic image on CRT monitor in order to confirm the potentiality of developing a remote human sensibility survey system through Internet. Therefore two experiments were conducted and the object for experiments was limited to automotive interior. The analysis results showed that there were significant differences between graphic image and real product in case of total interior and IPC(Instrument Panel Center) and no significant difference in case of display panel. Also, there were no significant difference when the subject group was female(housewife). To conclude, we can infer, in case of display panel, that it is possible to replace real product with graphic image to extract similar results on human sensibility and to collect human sensibility through Internet.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.150-153
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• 2003

As the web is vast and disorderly, it is difficult to find desired information on the web. In particular, finding image resources (knowing where and what kind of images can be found on the web) is very difficult but challenging. As the first step towards the web resource mining, this paper reports the preliminary results of collecting a number of images by a web robot as well as presenting those meta information.

• IEIE Transactions on Smart Processing and Computing
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• v.4 no.5
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• pp.340-348
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• 2015

The "stamp rally" is an event that participants go the round with predetermined points for the purpose of collecting stamps. They bring the stamp card to these points. They, however, sometimes leave or lose the card. In this case, they may not reach the final destination of the stamp rally. The purpose of this research is the construction of the stamp rally system which distinguishes each participant with his or her hand instead of the stamp card. We have realized our method distinguishing a hand posture by the image processing. We have also evaluated it by 30 examinees. Furthermore, we have designed the data communication between the server and the checkpoint to implement our whole system. We have also designed and implemented the process for the registering participant, the passing checkpoint and the administration.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.807-810
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• 2003

This research uses high-resolution satellite images as a source of collecting farmland information. For effectively extract the paddy area, we use texture information and different classify methods to assist the satellite image classification. First, using maximum likelihood classifier to extract paddy information from images. The results show that User Accuracy and Procedure Accuracy of the paddy area can increase from 80.60% to 95.45% and 84.38% to 95.45%. Second, establishing a paddy Knowledge Base and using Knowledge Base Classifier to extract paddy area, and result shows the User Accuracy and Producer Accuracy to be 92.16% and 90.06%. Finally, The result shows we can effectively contribute to the paddy field information extraction from high-resolution satellite images.

• Journal of the Korean Society of Industry Convergence
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• v.27 no.3
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• pp.563-572
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• 2024

Image processing and computer vision technologies are becoming increasingly important in a variety of application fields that require techniques and tools for sophisticated image analysis. In particular, image segmentation is a technology that plays an important role in image analysis. In this study, in order to identify recent research trends on image segmentation techniques, we used the Web of Science(WoS) database to analyze the R&D topography based on the network structure of the author's keyword co-occurrence matrix. As a result, from 2015 to 2023, as a result of the analysis of the R&D map of research articles on image segmentation, R&D in this field is largely focused on four areas of research and development: (1) researches on collecting and preprocessing image data to build higher-performance image segmentation models, (2) the researches on image segmentation using statistics-based models or machine learning algorithms, (3) the researches on image segmentation for medical image analysis, and (4) deep learning-based image segmentation-related R&D. The scientometrics-based analysis performed in this study can not only map the trajectory of R&D related to image segmentation, but can also serve as a marker for future exploration in this dynamic field.

• Journal of the Korean Society of Costume
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• v.64 no.5
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• pp.91-107
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• 2014

This purpose of this study is to analyze the pursuit of current fashion trends and fashion image types of middle-aged women in Korea. This study attempted to investigate the standards and properties of these different types of fashion images. The overall characteristics of middle-aged women and the concepts of personal image and fashion image were investigated through literature research. Survey and analysis based on Q methodology was conducted. Factors of personal image, fashion image and components of fashion image were analyzed by collecting information from in-depth workshops and focus group interview of an expert group. The results of this study are as follows: 1) The main factors influencing the current fashion image of women in their forties and fifties in Korea are classified into six types. 2) The elements of fashion image that Korean women in their 40s and 50s pursue are divide into four types. 3) Each type can be recognized by specific fashion image components and colors. 4) This shows that middle-aged Korean women are highly conscious of how others perceive them and have a desire to not stand out from others. It also shows that they are very active in pursuing fashion and following trends, which is the image of an active and dignified woman. This study provides the framework that enables sorting of the fashion images types that middle-aged Korean women want to pursue. The results from analyzing the factors can be used to recognize specific fashion images, and can be used in the planning and designing of fashion items for middle-aged Korean women.

• Journal of Korea Multimedia Society
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• v.8 no.3
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• pp.355-361
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• 2005

This paper proposed a system collecting image information by agents in multi-threaded environment and then retrieving them with content based image retrieval. This system uses multi threads to retrieve web information effectively, then improves efficiency of CPU cycles to reduce latency time, which is the time requesting queries, executing communication processing 4hat the retrieval agents perform and filtering the retrieval results. Also, the agents for image retrieval use Java language, which is platform independent, to be suitable for distributed environment. Using JDBC to save the retrieved images, the agents are connected to database. The images themselves are stored in distributed agents' databases, and only the image indexes are stored in an index server so that the efficiency of storage and retrieval time can be improved.

• Journal of Astronomy and Space Sciences
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• v.24 no.4
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• pp.379-388
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• 2007

Modulation Transfer Function (MTF) of satellite image is an important performance index in satellite image applications. Therefore MTF performance is assessed using satellite image for the ground target during LEOP phase after launch. But the MTF performance assessment using the ground target can be affected by imaging conditions such as cloud and weather. In this paper system requirements and satellite operation for assessing MTF performance of satellite image using stellar sources are proposed. Satellite capability in collecting stellar sources using the satellite which is designed for earth observation and satellite image usefulness for assessing MTF performances were analyzed. The proposed approach will be useful to assess MTF performance of earth observation satellite in lower earth orbit.

• Journal of the Korean Society of Clothing and Textiles
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• v.44 no.2
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• pp.354-368
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• 2020

Accurate and versatile image data-sets are essential for fashion AI research and AI-based fashion businesses based on a systematic attribute classification system. This study constructs a color and texture attribute hierarchical classification system by collecting fashion item images and analyzing the metadata of fashion items described by consumers. Essential dimensions to explain color and texture attributes were extracted; in addition, attribute values for each dimension were constructed based on metadata and previous studies. This hierarchical classification system satisfies consistency, exclusiveness, inclusiveness, and flexibility. The image tagging to confirm the usefulness of the proposed classification system indicated that the contents of attributes of the same image differ depending on the annotator that require a clear standard for distinguishing differences between the properties. This classification system will improve the reliability of the training data for machine learning, by providing standardized criteria for tasks such as tagging and annotating of fashion items.

• The Journal of Korea Robotics Society
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• v.13 no.1
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• pp.38-44
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• 2018

In this paper, we propose Intelligent Driver Assistance System (I-DAS) for driver safety. The proposed system recognizes safety and danger status by analyzing blind spots that the driver cannot see because of a large angle of head movement from the front. Most studies use image pre-processing such as face detection for collecting information about the driver's head movement. This not only increases the computational complexity of the system, but also decreases the accuracy of the recognition because the image processing system dose not use the entire image of the driver's upper body while seated on the driver's seat and when the head moves at a large angle from the front. The proposed system uses a convolutional neural network to replace the face detection system and uses the entire image of the driver's upper body. Therefore, high accuracy can be maintained even when the driver performs head movement at a large angle from the frontal gaze position without image pre-processing. Experimental result shows that the proposed system can accurately recognize the dangerous conditions in the blind zone during operation and performs with 95% accuracy of recognition for five drivers.