• Journal of Korean Society for Geospatial Information Science
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• v.1 no.2 s.2
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• pp.159-166
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• 1993

Decrease of forest is seriously caused by urbanization. Photographic interpretation is the act of examining photographic images for the purpose of identifying objects and judging their significance. A systematic study of aerial photographs usually involves a consideration of the basic characteristics of photographic images. Seven of these characteristics are shape, size, pattern, shadow, tone, texture, and site. Aerial photographs contain a detailed record of the ground at the time of exposure. Authors blow the changes of natural environment by database for interpretation of aerial photo. In this paper, authors choose the Pusan National University located at the Kum-Joung Koo, Pusan as model area. Ten year of interval in 1980 and 1990, authors know the rate of forest decreasing is approximately 41 percents and the necessity of the protection of foreast. Authors suggest the combination of construction and protection of environment.

• Culinary science and hospitality research
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• v.16 no.1
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• pp.13-23
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• 2010

The principal objective of this study was to develop the optimal recipe for muffins prepared with replacement of sucrose with trehalose. The effects of trehalose on properties and staling of muffins during storage days(0, 1, 3, 5 days) were evaluated in terms of height, volume, weight, specific volume, baking loss rate, crumbscan, colorimeter, texture analyzer and sensory evaluation. Crust thickness of muffins containing trehalose evaluated with crumbscan decreased as the content of trehalose increased. Lightness(L value) of muffins with trehalose increased for the storage days, but muffins without trehalose decreased. yellowness(b value) increased significantly as the trehalose content increased. Hardness value of muffins was reduced by adding trehalose; however, the resilience value of muffins with trehalose increased significantly. Finally, the sensory evaluation revealed that muffins with 25% of trehalose showed the best result in texture, taste and overall preference.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.7C
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• pp.675-686
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• 2005

In this paper, we propose an efficient face region detection technique for the content-based video summarization. To segment video, shot changes are detected from a video sequence and key frames are selected from the shots. We select one frame that has the least difference between neighboring frames in each shot. The proposed face detection algorithm detects face region from selected key frames. And then, we provide user with summarized frames included face region that has an important meaning in dramas or movies. Using Bayes classification rule and statistical characteristic of the skin pixels, face regions are detected in the frames. After skin detection, we adopt the projection method to segment an image(frame) into face region and non-face region. The segmented regions are candidates of the face object and they include many false detected regions. So, we design a classifier to minimize false lesion using CART. From SGLD matrices, we extract the textual feature values such as Inertial, Inverse Difference, and Correlation. As a result of our experiment, proposed face detection algorithm shows a good performance for the key frames with a complex and variant background. And our system provides key frames included the face region for user as video summarized information.

• Journal of the Korea Convergence Society
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• v.12 no.11
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• pp.45-51
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• 2021

With the development of deep learning, semantic segmentation methods are being studied in various fields. There is a problem that segmenation accuracy drops in fields that require accuracy such as medical image analysis. In this paper, we improved PSPNet, which is a deep learning based segmentation method to minimized the loss of features during semantic segmentation. Conventional deep learning based segmentation methods result in lower resolution and loss of object features during feature extraction and compression. Due to these losses, the edge and the internal information of the object are lost, and there is a problem that the accuracy at the time of object segmentation is lowered. To solve these problems, we improved PSPNet, which is a semantic segmentation model. The multi-scale attention proposed to the conventional PSPNet was added to prevent feature loss of objects. The feature purification process was performed by applying the attention method to the conventional PPM module. By suppressing unnecessary feature information, eadg and texture information was improved. The proposed method trained on the Cityscapes dataset and use the segmentation index MIoU for quantitative evaluation. As a result of the experiment, the segmentation accuracy was improved by about 1.5% compared to the conventional PSPNet.

• Journal of KIISE:Software and Applications
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• v.31 no.10
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• pp.1332-1344
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• 2004

Upon development of information super-highway and multimedia-related technoiogies in recent years, more efficient technologies to transmit, store and retrieve the multimedia data are required. Among such technologies, firstly, it is common that the semantic-based image retrieval is annotated separately in order to give certain meanings to the image data and the low-level property information that include information about color, texture, and shape Despite the fact that the semantic-based information retrieval has been made by utilizing such vocabulary dictionary as the key words that given, however it brings about a problem that has not yet freed from the limit of the existing keyword-based text information retrieval. The second problem is that it reveals a decreased retrieval performance in the content-based image retrieval system, and is difficult to separate the object from the image that has complex background, and also is difficult to extract an area due to excessive division of those regions. Further, it is difficult to separate the objects from the image that possesses multiple objects in complex scene. To solve the problems, in this paper, I established a content-based retrieval system that can be processed in 5 different steps. The most critical process of those 5 steps is that among RGB images, the one that has the largest and the smallest background are to be extracted. Particularly. I propose the method that extracts the subject as well as the background by using an Image, which has the largest background. Also, to solve the second problem, I propose the method in which multiple objects are separated using RGB channel selection techniques having optimized the excessive division of area by utilizing Watermerge's threshold value with the object separation using the method of RGB channels separation. The tests proved that the methods proposed by me were superior to the existing methods in terms of retrieval performances insomuch as to replace those methods that developed for the purpose of retrieving those complex objects that used to be difficult to retrieve up until now.