• Korean Journal of Remote Sensing
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• v.31 no.6
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• pp.531-548
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• 2015

Atmospheric Motion Vector (AMV) from satellite images have shown Slow Speed Bias (SSB) in comparison with rawinsonde. The causes of SSB are originated from tracking, selection, and height assignment error, which is known to be the leading error. However, recent works have shown that height assignment error cannot be fully explained the cause of SSB. This paper attempts a new approach to examine the possibility of SSB reduction of COMS AMV by using a new target tracking algorithm. Tracking error can be caused by averaging of various wind patterns within a target and changing of cloud shape in searching process over time. To overcome this problem, Gaussian Mixture Model (GMM) has been adopted to extract the coldest cluster as target since the shape of such target is less subject to transformation. Then, an image filtering scheme is applied to weigh more on the selected coldest pixels than the other, which makes it easy to track the target. When AMV derived from our algorithm with sum of squared distance method and current COMS are compared with rawindsonde, our products show noticeable improvement over COMS products in mean wind speed by an increase of $2.7ms^{-1}$ and SSB reduction by 29%. However, the statistics regarding the bias show negative impact for mid/low level with our algorithm, and the number of vectors are reduced by 40% relative to COMS. Therefore, further study is required to improve accuracy for mid/low level winds and increase the number of AMV vectors.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.175-197
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• 2021

Recently, with the development of deep learning technology, research on unstructured data analysis is being actively conducted, and it is showing remarkable results in various fields such as classification, summary, and generation. Among various text analysis fields, text classification is the most widely used technology in academia and industry. Text classification includes binary class classification with one label among two classes, multi-class classification with one label among several classes, and multi-label classification with multiple labels among several classes. In particular, multi-label classification requires a different training method from binary class classification and multi-class classification because of the characteristic of having multiple labels. In addition, since the number of labels to be predicted increases as the number of labels and classes increases, there is a limitation in that performance improvement is difficult due to an increase in prediction difficulty. To overcome these limitations, (i) compressing the initially given high-dimensional label space into a low-dimensional latent label space, (ii) after performing training to predict the compressed label, (iii) restoring the predicted label to the high-dimensional original label space, research on label embedding is being actively conducted. Typical label embedding techniques include Principal Label Space Transformation (PLST), Multi-Label Classification via Boolean Matrix Decomposition (MLC-BMaD), and Bayesian Multi-Label Compressed Sensing (BML-CS). However, since these techniques consider only the linear relationship between labels or compress the labels by random transformation, it is difficult to understand the non-linear relationship between labels, so there is a limitation in that it is not possible to create a latent label space sufficiently containing the information of the original label. Recently, there have been increasing attempts to improve performance by applying deep learning technology to label embedding. Label embedding using an autoencoder, a deep learning model that is effective for data compression and restoration, is representative. However, the traditional autoencoder-based label embedding has a limitation in that a large amount of information loss occurs when compressing a high-dimensional label space having a myriad of classes into a low-dimensional latent label space. This can be found in the gradient loss problem that occurs in the backpropagation process of learning. To solve this problem, skip connection was devised, and by adding the input of the layer to the output to prevent gradient loss during backpropagation, efficient learning is possible even when the layer is deep. Skip connection is mainly used for image feature extraction in convolutional neural networks, but studies using skip connection in autoencoder or label embedding process are still lacking. Therefore, in this study, we propose an autoencoder-based label embedding methodology in which skip connections are added to each of the encoder and decoder to form a low-dimensional latent label space that reflects the information of the high-dimensional label space well. In addition, the proposed methodology was applied to actual paper keywords to derive the high-dimensional keyword label space and the low-dimensional latent label space. Using this, we conducted an experiment to predict the compressed keyword vector existing in the latent label space from the paper abstract and to evaluate the multi-label classification by restoring the predicted keyword vector back to the original label space. As a result, the accuracy, precision, recall, and F1 score used as performance indicators showed far superior performance in multi-label classification based on the proposed methodology compared to traditional multi-label classification methods. This can be seen that the low-dimensional latent label space derived through the proposed methodology well reflected the information of the high-dimensional label space, which ultimately led to the improvement of the performance of the multi-label classification itself. In addition, the utility of the proposed methodology was identified by comparing the performance of the proposed methodology according to the domain characteristics and the number of dimensions of the latent label space.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.669-681
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• 2023

Studies have tried to apply remote sensing technology, a non-destructive survey method, instead of the existing destructive survey, which requires relatively large labor input and a long time to estimate chlorophyll content, which is an important indicator for evaluating the growth of fruit trees. This study was conducted to non-destructively evaluate the chlorophyll content of pear tree leaves using unmanned aerial vehicle-based hyperspectral imagery for two years(2021, 2022). The reflectance of the single bands of the pear tree canopy extracted through image processing was band rationed to minimize unstable radiation effects depending on time changes. The estimation (calibration and validation) models were developed using machine learning algorithms of elastic-net, k-nearest neighbors(KNN), and support vector machine with band ratios as input variables. By comparing the performance of estimation models based on full band ratios, key band ratios that are advantageous for reducing computational costs and improving reproducibility were selected. As a result, for all machine learning models, when calibration of coefficient of determination (R²)≥0.67, root mean squared error (RMSE)≤1.22 ㎍/cm², relative error (RE)≤17.9% and validation of R²≥0.56, RMSE≤1.41 ㎍/cm², RE≤20.7% using full band ratios were compared, four key band ratios were selected. There was relatively no significant difference in validation performance between machine learning models. Therefore, the KNN model with the highest calibration performance was used as the standard, and its key band ratios were 710/714, 718/722, 754/758, and 758/762 nm. The performance of calibration showed R²=0.80, RMSE=0.94 ㎍/cm², RE=13.9%, and validation showed R²=0.57, RMSE=1.40 ㎍/cm², RE=20.5%. Although the performance results based on validation were not sufficient to estimate the chlorophyll content of pear tree leaves, it is meaningful that key band ratios were selected as a standard for future research. To improve estimation performance, it is necessary to continuously secure additional datasets and improve the estimation model by reproducing it in actual orchards. In future research, it is necessary to continuously secure additional datasets to improve estimation performance, verify the reliability of the selected key band ratios, and upgrade the estimation model to be reproducible in actual orchards.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.10
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• pp.948-956
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• 2014

Today, Intelligent Vehicle Detection System seeks to reduce the negative factors, such as accidents over to get the traffic information of existing system. This paper proposes detection algorithm for the illegal U-turn vehicles which can cause critical accident among violations of road traffic laws. We predicted that if calculated optical flow vectors were shown on the illegal U-turn path, they would be cause of the illegal U-turn vehicles. To reduce the high computational complexity, we use the algorithm of pyramid Lucas-Kanade. This algorithm only track the key-points likely corners. Because of the high computational complexity, we detect center lane first through the color information and progressive probabilistic hough transform and apply to the around of center lane. And then we select vectors on illegal U-turn path and calculate reliability to check whether vectors is cause of the illegal U-turn vehicles or not. Finally, In order to evaluate the algorithm, we calculate process time of the type of algorithm and prove that proposed algorithm is efficiently.

• Journal of the Korean Association of Geographic Information Studies
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• v.3 no.3
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• pp.69-76
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• 2000

This study deals with development GIS application using web-based CAD, this application serves to user, designer, manager that more convenient and various functions. Development to this application, collect attribute data from fieldwork and geographic data from cadastral map and aerial survey map and then development to user interface using HTML, JavaScript, ASP, Whip ActiveX control. This application's characters are as follows ; First, system designer designed that anyone who have basic knowledge about web and CAD can develop this application. A system structure simplification by 2-Tier. Geographic information use DWF(drawing web format) file and attribute information use DBMS in consideration of extension. Second, system manager can service independently GIS in Web need not high priced GIS engine, so more economical. Third, internet user get service GIS information and function that search of information, zoom in/out, pan, print etc., if you need more functions, add function without difficultly. Developed application as above, not only save volume but fast of speed as use vector data exclude character and image data. Also, this application can used by means of commercial and travel information service but also various GIS service of public institution and private in web.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.12
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• pp.889-898
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• 2013

Due to the popularization of high-performance capturing equipments and the emergence of powerful image-editing softwares, it is easy to make high-quality counterfeit money. However, the probability of detecting counterfeit money to the general public is extremely low and the detection device is expensive. In this paper, a counterfeit money detection algorithm using a general purpose scanner and computer system is proposed. First, the printing features of color printers are calculated using morphological operations and gray-level co-occurrence matrix. Then, these features are used to train a support vector machine classifier. This trained classifier is applied for identifying either original or counterfeit money. In the experiment, we measured the detection rate between the original and counterfeit money. Also, the printing source was identified. The proposed algorithm was compared with the algorithm using wiener filter to identify color printing source. The accuracy for identifying counterfeit money was 91.92%. The accuracy for identifying the printing source was over 94.5%. The results support that the proposed algorithm performs better than previous researches.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.39 no.6
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• pp.623-629
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• 2002

In transmitting compressed video bit-stream over Internet, packet losses cause error propagations in both spatial and temporal domains, which in turn leads to severe degradation I image quality. In this paper, a new error concealment algorithm, called EBMA(Edge Detection based Boundary Matching Algorithm), is proposed to repair damaged portions of the video frames in the receiver. Conventional BMA(Boundary Matching Algorithm) assumes that the pixels on the boundary of the missing block and its neighboring blocks are very similar, but has no consideration of edges across the boundary. In our approach, the edges are detected across the boundary of the lost or erroneous block. Once the orientation of each edge is found, only the pixel difference along the expected edges across the boundary is measured instead of the calculation of difference along the expected edges across the boundary is measured instead of the calculation of differences between all adjacent pixels on the boundary Therefore, the proposed approach needs very few computations and the experiment shows and improvement of the performance over the conventional BMA in terms of both subjective and objective quality of video sequences.

• Journal of Broadcast Engineering
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• v.19 no.6
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• pp.798-809
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• 2014

Video stabilization is performed to remove unexpected shaky and irregular motion from a video. It is often used as preprocessing for robust feature tracking and matching in video. Typical video stabilization algorithms are developed to compensate motion from surveillance video or outdoor recordings that are captured by a hand-help camera. However, since the vehicle video contains rapid change of motion and local features, typical video stabilization algorithms are hard to be applied as it is. In this paper, we propose a novel approach to compensate shaky and irregular motion in vehicle video using linear regression model and vertical projection histogram matching. Towards this goal, we perform vertical projection histogram matching at each sub region of an input frame, and then we generate linear regression model to extract vertical translation and rotation parameters with estimated regional vertical movement vector. Multiple binarization with sub-region analysis for generating the linear regression model is effective to typical recording environments where occur rapid change of motion and local features. We demonstrated the effectiveness of our approach on blackbox videos and showed that employing the linear regression model achieved robust estimation of motion parameters and generated stabilized video in full automatic manner.

• Journal of the Korea Convergence Society
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• v.9 no.1
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• pp.419-431
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• 2018

In this paper, we propose a novel method for monitoring mood trend of Twitter users by analyzing their daily tweets for a long period. Then, to more accurately understand their tweets, we analyze all types of content in tweets, i.e., texts and emoticons, and images, thus develop a multimodal sentiment analysis method. In the proposed method, two single-modal analyses first are performed to extract the users' moods hidden in texts and images: a lexicon-based and learning-based text classifier and a learning-based image classifier. Thereafter, the extracted moods from the respective analyses are combined into a tweet mood and aggregated a daily mood. As a result, the proposed method generates a user daily mood flow graph, which allows us for monitoring the mood trend of users more intuitively. For evaluation, we perform two sets of experiment. First, we collect the data sets of 40,447 data. We evaluate our method via comparing the state-of-the-art techniques. In our experiments, we demonstrate that the proposed multimodal analysis method outperforms other baselines and our own methods using text-based tweets or images only. Furthermore, to evaluate the potential of the proposed method in monitoring users' mood trend, we tested the proposed method with 40 depressive users and 40 normal users. It proves that the proposed method can be effectively used in finding depressed users.

• Journal of Broadcast Engineering
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• v.19 no.3
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• pp.329-341
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• 2014

Kernel-based mean-shift object tracking has gained more interests nowadays, with the aid of its feasibility of reliable real-time implementation of object tracking. This algorithm calculates the best mean-shift vector based on the color histogram similarity between target model and target candidate models, where the color histograms are usually produced after uniform color-space quantization for the implementation of real-time tracker. However, when the image of target model has a reduced contrast, such uniform quantization produces the histogram model having large values only for a few histogram bins, resulting in a reduced accuracy of similarity comparison. To solve this problem, a non-uniform quantization algorithm has been proposed, but it is hard to apply to real-time tracking applications due to its high complexity. Therefore, this paper proposes a fast non-uniform color-space quantization method using the histogram equalization, providing an adjusted histogram distribution such that the bins of target model histogram have as many meaningful values as possible. Using the proposed method, the number of bins involved in similarity comparison has been increased, resulting in an enhanced accuracy of the proposed mean-shift tracker. Simulations with various test videos demonstrate the proposed algorithm provides similar or better tracking results to the previous non-uniform quantization scheme with significantly reduced computation complexity.