• Journal of Korea Society of Industrial Information Systems
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• v.19 no.2
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• pp.85-91
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• 2014

The objective of the research is to automatically recognize railway objects from MLS data in which 9 key objects including terrain, track, bed, vegetation, platform, barrier, posts, attachments, powerlines are targeted. The proposed method can be divided into two main sub-steps. First, multi-scale contextual features are extracted to take the advantage of characterizing objects of interest from different geometric levels such as point, line, volumetric and vertical profile. Second, by considering contextual interactions amongst object labels, a contextual classifier is utilized to make a prediction with local coherence. In here, the Conditional Random Field (CRF) is used to incorporate the object context. By maximizing the object label agreement in the local neighborhood, CRF model could compensate the local inconsistency prediction resulting from other local classifiers. The performance of proposed method was evaluated based on the analysis of commission and omission error and shows promising results for the practical use.

• Journal of KIISE:Software and Applications
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• v.31 no.5
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• pp.563-569
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• 2004

A set of Zernike moments has been successfully used for object recognition or content-based image retrieval systems. Real time applications using Zernike moments, however, have been limited due to its complicated definition. Conventional methods to compute Zernike moments fast have focused mainly on the radial components of the moments. In this paper, utilizing symmetric/anti-symmetric properties of Zernike basis functions, we propose a fast and efficient method for Zernike moments. By reducing the number of operations to one quarter of the conventional methods in the proposed method, the computation time to generate Zernike basis functions was reduced to about 20% compared with conventional methods. In addition, the amount of memory required for efficient computation of the moments is also reduced to a quarter. We also showed that the algorithm can be extended to compute the similar classes of rotational moments, such as pseudo-Zernike moments, and ART descriptors in same manner.

• Smart Media Journal
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• v.5 no.1
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• pp.24-29
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• 2016

To satisfy the needs of high-level intelligent surveillance system, it shall be able to extract objects and classify to identify precise information on the object. The representative method to identify one's identity is face recognition that is caused a change in the recognition rate according to environmental factors such as illumination, background and angle of camera. In this paper, we analyze the robust face recognition of face image by changing the distance through a variety of experiments. The experiment was conducted by real face images of 1m to 5m. The method of face recognition based on Linear Discriminant Analysis show the best performance in average 75.4% when a large number of face images per one person is used for training. However, face recognition based on Convolution Neural Network show the best performance in average 69.8% when the number of face images per one person is less than five. In addition, rate of low resolution face recognition decrease rapidly when the size of the face image is smaller than $15{\times}15$.

• Journal of Broadcast Engineering
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• v.26 no.4
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• pp.419-428
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• 2021

Shadow removal plays a key role for the pre-processing of image processing techniques such as object tracking and detection. With the advances of image recognition based on deep convolution neural networks, researches for shadow removal have been actively conducted. In this paper, we propose a novel method for shadow removal, which utilizes self attention distillation to extract semantic features. The proposed method gradually refines results of shadow detection, which are extracted from each layer of the proposed network, via top-down distillation. Specifically, the training procedure can be efficiently performed by learning the contextual information for shadow removal without shadow masks. Experimental results on various datasets show the effectiveness of the proposed method for shadow removal under real world environments.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.45 no.9
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• pp.794-806
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• 2017

It is essential to protect the national space assets and space environment safely as a space development country from the continuously increasing space debris. And Active Debris Removal(ADR) is the most active way to solve this problem. In this paper, we studied the Artificial Neural Network(ANN) for a stable recognition model of vision-based space debris tracking system. We obtained the simulated image of the space environment by the KARICAT which is the ground-based space debris clearing satellite testbed developed by the Korea Aerospace Research Institute, and created the vector which encodes structure and color-based features of each object after image segmentation by depth discontinuity. The Feature Vector consists of 3D surface area, principle vector of point cloud, 2D shape and color information. We designed artificial neural network model based on the separated Feature Vector. In order to improve the performance of the artificial neural network, the model is divided according to the categories of the input feature vectors, and the ensemble technique is applied to each model. As a result, we confirmed the performance improvement of recognition model by ensemble technique.

• Journal of KIISE:Software and Applications
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• v.29 no.12
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• pp.907-925
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• 2002

Due to the emersion of J2EE(Java 2, Enterprise Edition), many enterprises inside and outside of the country have been developing the enterprise applications appropriate to the J2EE model. With the help of the component model of Enterprise Java Beans(EJH) which is the J2EE core technology, we can develop the distributed object applications quite simple. EJB application can be implemented by using the component-oriented object transaction middleware and the most applications utilize the distributed transaction. EJB developers can concentrate on the business logic because the EJB server covers the middleware service. Due to these characteristics, EJB technology became popular and then the study for EJB based application has been done quite actively However, the research of metrics for measuring the performance during run-time of the EJB applications has not been done enough. Tn this paper, we explore the workflow for the EJB application service on the run-time and classify the internal operation into several elements. We propose the metrics for evaluating the performance up to the bean level by using the classified elements. First, we analyze the lifecycle according to the bean types which comes from the EJB application on the run-time as to extract each factor used in performance measurement. We also find factors related to a performance and allocate the Performance factors to the metrics as the bean types. We also consider the characteristics like the bean's activation and message passing which happens during bean message call and then analyze the relations of the beans participating in the workflow of the application to make the workflow performance measurement possible. And we devise means to bring performance enhancement of the EJB application using the propose.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.24 no.1
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• pp.111-121
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• 2006

Recently, many studies have been performed to apply airborne LIDAR data to extracting urban models. In order to model efficiently the man-made objects which are the main components of these urban models, it is important to extract automatically planar patches from the set of the measured three-dimensional points. Although some research has been carried out for their automatic extraction, no method published yet is sufficiently satisfied in terms of the accuracy and completeness of the segmentation results and their computational efficiency. This study thus aimed to developing an efficient approach to automatic segmentation of planar patches from the three-dimensional points acquired by an airborne LIDAR system. The proposed method consists of establishing adjacency between three-dimensional points, grouping small number of points into seed patches, and growing the seed patches into surface patches. The core features of this method are to improve the segmentation results by employing the variable threshold value repeatedly updated through a statistical analysis during the patch growing process, and to achieve high computational efficiency using priority heaps and sequential least squares adjustment. The proposed method was applied to real LIDAR data to evaluate the performance. Using the proposed method, LIDAR data composed of huge number of three dimensional points can be converted into a set of surface patches which are more explicit and robust descriptions. This intermediate converting process can be effectively used to solve object recognition problems such as building extraction.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.6 s.312
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• pp.83-91
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• 2006

Flashlights in video has many problem to detect the scene change because of high difference values from successive frames. In this paper propose the reliable scene change detection algorithms by extracting the flashlights. This paper proposes a robust scene change detection technique that uses the weighted chi-square test and the automated threshold-decision algorithms. The weighted chi-square test can subdivide the difference values of individual color channels by calculating the color intensities according to NTSC standard, and it can detect the scene change by joining the weighted color intensities to the predefined chi-square test which emphasize the comparative color difference values. The automated threshold-decision algorithm uses the difference values of frame-to-frame that was obtained by the weighted chi-square test. At first, The Average of total difference values is calculated and then, another average value is calculated using the previous average value from the difference values, finally the most appropriate mid-average value is searched and considered the threshold value. Experimental results show that the proposed algorithms are effective and outperform the previous approaches.

• 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 Society of Computer and Information
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• v.14 no.3
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• pp.93-101
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• 2009

This paper suggests a new algorithm automatically searching for Region-of-Interest(ROI) with high speed, using the edge information of high frequency subband transformed with wavelet. The proposed method executes a searching algorithm of 4-direction object boundary by the unit of block using the edge information, and detects ROIs. The whole image is splitted by $64{\times}64$ or $32{\times}32$ sized blocks and the blocks can be ROI block or background block according to taking the edges or not. The 4-directions searche the image from the outside to the center and the algorithm uses a feature that the low-DOF image has some edges as one goes to center. After searching all the edges, the method regards the inner blocks of the edges as ROI, and makes the ROI masks and sends them to server. This is one of the dynamic ROI method. The existing methods have had some problems of complicated filtering and region merge, but this method improved considerably the problems. Also, it was possible to apply to an application requiring real-time processing caused by the process of the unit of block.