• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.439-442
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• 1999

It is important to analyze the seasonal change profiles of land cover type in large scale for establishing preservation strategy and environmental monitoring. Because the NOAA-AVHRR data sets provide global data with high temporal resolution, it is suitable for the land cover classification of the large area. The objectives of this study were to classify land cover of Korea, to investigate the phenological profiles of land cover. The NOAA-AVHRR data from Jan. 1998 to Dec. 1998 were received by Korea Ocean Research & Development Institute(KORDI) and were used for this study. The NDVI data were produced from this data. And monthly maximum value composite data were made for reducing cloud effect and temporal classification. And the data were classified using the method of supervised classification. To label the land cover classes, they were classified again using generalized vegetation map and Landsat-TM classified image. And the profiles of each class was analyzed according to each month. Results of this study can be summarized as follows. First, it was verified that the use of vegetation map and TM classified map was available to obtain the temporal class labeling with NOAA-AVHRR. Second, phenological characteristics of plant communities of Korea using NOAA-AVHRR was identified. Third, NDVI of North Korea is lower on Summer than that of South Korea. And finally, Forest cover is higher than another cover types. Broadleaf forest is highest on may. Outline of covertype profiles was investigated.

• Advances in robotics research
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• v.1 no.2
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• pp.127-140
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• 2014

This work presents an IR-based system for parking assistance and obstacle detection in the automotive field that employs the Microsoft Kinect camera for fast 3D point cloud reconstruction. In contrast to previous research that attempts to explicitly identify obstacles, the proposed system aims to detect "reachable regions" of the environment, i.e., those regions where the vehicle can drive to from its current position. A user-friendly 2D traversability grid of cells is generated and used as a visual aid for parking assistance. Given a raw 3D point cloud, first each point is mapped into individual cells, then, the elevation information is used within a graph-based algorithm to label a given cell as traversable or non-traversable. Following this rationale, positive and negative obstacles, as well as unknown regions can be implicitly detected. Additionally, no flat-world assumption is required. Experimental results, obtained from the system in typical parking scenarios, are presented showing its effectiveness for scene interpretation and detection of several types of obstacle.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2012.07a
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• pp.314-316
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• 2012

본 논문에서는 의료영상 영역화 기법으로 이용되는 레이블 융합 기법을 기반으로, 정합 기법을 이용했을 시 빈번하게 발생하는 경계에서의 오차를 크게 줄여줄 수 있는 기법을 제안한다. 패치 기반 레이블 융합 기법은 패치 간의 밝기 값의 유사도를 기반으로 융합 가중치를 계산하였지만 이는 밝기의 분포가 상대적으로 다른 자기공명영상에 적합하지 못한 경우가 많았다. 본 논문에서는 밝기 값과 함께 밝기 값의 경사도 유사도를 추가적으로 계산하여 융합 가중치를 얻어내는 기법을 제안한다. 밝기의 분포가 다른 영역에서도 밝기의 경사도 분포는 대부분 유사하기 때문에, 오차가 많은 모호한 경계에서 향상된 결과를 얻을 수 있었다. 제안하는 기법의 성능평가를 위해 50 개의 SKI10 무릎 관절 데이터 셋 내에서 대퇴골을 영역화 하였다. 실험 결과를 통해 제안하는 기법이 밝기 값 유사도 정보만을 이용했던 기법에 비해 개선된 성능을 보이고 있음을 확인할 수 있다.

• Proceedings of the Korean Information Science Society Conference
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• 2002.10d
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• pp.160-162
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• 2002

지리정보시스템(GIS)에서 사용하는 지도는 여러 가지 목적의 응용 프로그램이나 지도의 축척등에 의해 서 다양하게 나타난다. 본 논문은 지리정보 시스템에 사용되는 지도중에서 관광안내지도를 대상으로 최적의 레이블링과 아이콘 표시방법을 통하여 관광지도 정보를 최적화하여 표현하기 위한 논문이다. 이를 위하여 서울시내의 여러 가지 관광버스의 노선도를 분석하여 이를 최적화된 방법으로 배치하고 표현하는 위한 방법을 연구하였다. 복잡한 버스의 노선은 주요 시설물을 중심으로 선분 간략화 알고리즘을 통하여 간략화 하였으며 간략화된 노선에서 중첩되는 레이블은 레이블 재배치 알고리즘을 이용하여 재배열하였다. 이러한 방법을 통하여 지리정보 데이터베이스로부터 자동화된 방식으로 구조화된 지도를 손쉽게 생성하는 방법을 제시한다.

• Journal of applied mathematics & informatics
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• v.37 no.1_2
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• pp.149-156
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• 2019

Let G be a (p, q) graph. Let $f:V(G){\rightarrow}\{1,2,{\ldots},k\}$ be a map where $k{\in}{\mathbb{N}}$ and k > 1. For each edge uv, assign the label gcd(f(u), f(v)). f is called k-Total prime cordial labeling of G if ${\mid}t_f(i)-t_f(j){\mid}{\leq}1$, $i,j{\in}\{1,2,{\ldots},k\}$ where $t_f$(x) denotes the total number of vertices and the edges labelled with x. A graph with a k-total prime cordial labeling is called k-total prime cordial graph. In this paper we investigate the 4-total prime cordial labeling of some graphs.

• Journal of Applied and Pure Mathematics
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• v.4 no.1_2
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• pp.51-61
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• 2022

Let G be a graph. Let f : V (G) → {0, 1, 2, …, k-1} be a map where k ∈ ℕ and k > 1. For each edge uv, assign the label |f(u) - f(v)|. f is called k-total difference cordial labeling of G if |t_df (i) - t_df (j) | ≤ 1, i, j ∈ {0, 1, 2, …, k - 1} where t_df (x) denotes the total number of vertices and the edges labeled with x. A graph with admits a k-total difference cordial labeling is called k-total difference cordial graphs. In this paper we investigate the 4-total difference cordial labeling behaviour of shell butterfly graph, Lilly graph, Shackle graphs etc..

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.27 no.2
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• pp.146-159
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• 2023

This paper proposes a hybrid algorithm combining K-means clustering and watershed algorithms for flower segmentation and counting. We use the K-means clustering algorithm to obtain the main colors in a complex background according to the cluster centers and then take a color space transformation to extract pixel values for the hue, saturation, and value of flower color. Next, we apply the threshold segmentation technique to segment flowers precisely and obtain the binary image of flowers. Based on this, we take the Euclidean distance transformation to obtain the distance map and apply it to find the local maxima of the connected components. Afterward, the proposed algorithm adaptively determines a minimum distance between each peak and apply it to label connected components using the watershed segmentation with eight-connectivity. On a dataset of 30 images, the test results reveal that the proposed method is more efficient and precise for the counting of overlapped flowers ignoring the degree of overlap, number of overlap, and relatively irregular shape.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.6
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• pp.227-233
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• 2019

Recently, along with the recent development of deep learning technique, neural networks are achieving success in computer vision filed. Convolutional neural network have shown outstanding performance in not only for a simple image classification task, but also for tasks with high difficulty such as object segmentation and detection. However many such deep learning models are based on supervised-learning, which requires more annotation labels than image-level label. Especially image semantic segmentation model requires pixel-level annotations for training, which is very. To solve these problems, this paper proposes a weakly-supervised semantic segmentation method which requires only image level label to train network. Existing weakly-supervised learning methods have limitations in detecting only specific area of object. In this paper, on the other hand, we use multi-classifier deep learning architecture so that our model recognizes more different parts of objects. The proposed method is evaluated using VOC 2012 validation dataset.

• Journal of Ginseng Research
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• v.43 no.1
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• pp.143-153
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• 2019

Background: Ginseng is one of the well-known medicinal plants, exhibiting diverse medicinal effects. Its roots possess anticancer and antiaging properties and are being used in the medical systems of East Asian countries. It is grown in low-light and low-temperature conditions, and its growth is strongly inhibited at temperatures above $25^{\circ}C$. However, the molecular responses of ginseng to heat stress are currently poorly understood, especially at the protein level. Methods: We used a shotgun proteomics approach to investigate the effect of heat stress on ginseng leaves. We monitored their photosynthetic efficiency to confirm physiological responses to a high-temperature stress. Results: The results showed a reduction in photosynthetic efficiency on heat treatment ($35^{\circ}C$) starting at 48 h. Label-free quantitative proteome analysis led to the identification of 3,332 proteins, of which 847 were differentially modulated in response to heat stress. The MapMan analysis showed that the proteins with increased abundance were mainly associated with antioxidant and translation-regulating activities, whereas the proteins related to the receptor and structural-binding activities exhibited decreased abundance. Several other proteins including chaperones, G-proteins, calcium-signaling proteins, transcription factors, and transfer/carrier proteins were specifically downregulated. Conclusion: These results increase our understanding of heat stress responses in the leaves of ginseng at the protein level, for the first time providing a resource for the scientific community.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.10
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• pp.4968-4986
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• 2017

In this paper, we primarily address the difficulty of automatic generation of a plausible depth map from a single image in an unstructured environment. The aim is to extrapolate a depth map with a more correct, rich, and distinct depth order, which is both quantitatively accurate as well as visually pleasing. Our technique, which is fundamentally based on a preexisting DepthTransfer algorithm, transfers depth information at the level of superpixels. This occurs within a framework that replaces a pixel basis with one of instance-based learning. A vital superpixels feature enhancing matching precision is posterior incorporation of predictive semantic labels into the depth extraction procedure. Finally, a modified Cross Bilateral Filter is leveraged to augment the final depth field. For training and evaluation, experiments were conducted using the Make3D Range Image Dataset and vividly demonstrate that this depth estimation method outperforms state-of-the-art methods for the correlation coefficient metric, mean log10 error and root mean squared error, and achieves comparable performance for the average relative error metric in both efficacy and computational efficiency. This approach can be utilized to automatically convert 2D images into stereo for 3D visualization, producing anaglyph images that are visually superior in realism and simultaneously more immersive.