• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.9
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• pp.901-908
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• 2016

Edge is the basic characteristic of image, edge detection is very important in image processing applications and computer vision area. Many studies are being performed to detect these edges by domestic and foreign researchers. The conventional edge detection methods such as Roberts, Sobel, Prewitt, and Laplacian etc, which are using a fixed value of mask are widely used and morphological gradient which uses dilation and erosion among morphology process techniques is also widely used. But these methods does not detect edges well in the diagonal direction or gradually changing image parts. Accordingly, in this paper, the modified top-hat and bottom-hat transform algorithms which are detecting edges well in the parts of diagonal direction or gradually changing image are proposed. The proposed algorithms present the detected edge images compared with the conventional methods and are evaluated performance by using cosine similarity.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.6
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• pp.1493-1498
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• 2015

The autofocus system is automatically to drive the focus. It is very important to computer vision system. In the case of a compact camera, the actuator technology is used for auto-focus in mass production. the position sensor is required, the circuit configuration and easy method is widely used in VCM, compared to the stability of the drive size and the noise is a big stepping motor type, size has a piezo system having a humidity problem and the small leaded vulnerability. In addition, there is a liquid lens system, the advantages of low power in a compact structure but also a structure with proven quality and reliability and features required pressure. In this paper, we implement two control systems that can control the actuator as a liquid range of VCM using a sharpness of the image acquired by the image sensor automatically initiates 5Mpixel class was the implementation verification of focusing.

• Journal of Korea Multimedia Society
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• v.9 no.4
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• pp.377-387
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• 2006

Digital documents takes place of paper (off-line) documents, because of the advantages of digital (on-line) documents: supply of information using dynamic contents and good to communize. However, users prefer paper documents to digital documents with the advantages of paper documents: inexpensive, handy to carry, and good to read. Therefore, for providing advantages of digital documents to users who prefer paper documents, many laboratories study about methods which augment digital documents to paper documents. In this paper, we propose the Hybrid Documents Authoring Tool (HDAT), which can insert, delete, and modify on-line information to the off-line. The proposed system is a unified authoring tool for reading and writing of on-line information. And we provide the most natural environment to users using computer vision technology without additional devices such as markers or patterns to retrieve documents. As shown by experimental results, we make sure that our proposed system has high usability and good efficiency on various environments through we measure the low-level of system requirement.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.8
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• pp.393-400
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• 2016

Recently, there have been various researches on efficient and automatic analysis on urban environment methods that utilize the computer vision and machine learning technology. Among many new analyses, urban safety analysis has received a major attention. In order to predict more accurately on safety score and reflect the human visual perception, it is necessary to consider the generic and local information that are most important to human perception. In this paper, we use Double-column Convolutional Neural network consisting of generic and local columns for the prediction of urban safety. The input of generic and local column used re-sized and random cropped images from original images, respectively. In addition, a new learning method is proposed to solve the problem of over-fitting in a particular column in the learning process. For the performance comparison of our Double-column Convolutional Neural Network, we compare two Support Vector Regression and three Convolutional Neural Network models using Root Mean Square Error and correlation analysis. Our experimental results demonstrate that our Double-column Convolutional Neural Network model show the best performance with Root Mean Square Error of 0.7432 and Pearson/Spearman correlation coefficient of 0.853/0.840.

• Journal of Digital Convergence
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• v.16 no.3
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• pp.95-100
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• 2018

The future society will be identify various problem situations accurately and the ability to solve problems effectively using computing technology become more important. Based on this background, an education of future human resources based on computational thinking as well as a problem-solving ability are important in university education. Therefore, in this paper, we have analyzed the effects of software education to improve computational thinking based on problem-solving learning. We have found that there is a significant difference between the interest of the students and their learning and academic achievements. Therefore, based on the understanding of the learning motivation and method, if the problem-solving learning is conducted in real-life scenarios suitable for the level of the student, it can be possible to induce the interest of the students and improve their computational thinking ability.

• 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.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.6
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• pp.135-139
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• 2012

The wide diffusion of internet and the overall increased reliance on technology for information communication, dissemination and gathering have created an unparalleled mass of data. Sifting through this data is defining and will define in the foreseeable future a big part of contemporary computer science. Within this data, a growing proportion is given by personal information, which represents a unique opportunity to study human activities extensively and live. One important recurring challenge in many disciplines is the problem of people re-identification. In its broadest definition, re-identification is the problem of newly recognizing previously identified people, such as following an unknown person while he walks through many different surveillance cameras in different locations. Our goals is to review how several diverse disciplines define and meet this challenge, from person re-identification in video-surveillance to authorship attribution in text samples to distinguishing users based on their preferences of pictures. We further envision a situation where multidisciplinary solutions might be beneficial.

• The Korean Journal of Applied Statistics
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• v.24 no.4
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• pp.695-708
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• 2011

Image fusion is the process of combining multiple images of the same scene into a single fused image with application to many fields, such as remote sensing, computer vision, robotics, medical imaging and military affairs. The widely used image fusion rules that use wavelet transform have been based on a simple comparison with the activity measures of local windows such as mean and standard deviation. In this case, information features from the original images are excluded in the fusion image and distorted fusion images are obtained for noisy images. In this paper, we propose the use of a nonparametric squared ranks test on the quality of variance for two samples in order to overcome the influence of the noise and guarantee the homogeneity of the fused image. We evaluate the method both quantitatively and qualitatively for image fusion as well as compare it to some existing fusion methods. Experimental results indicate that the proposed method is effective and provides satisfactory fusion results.

• International Journal of Highway Engineering
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• v.10 no.3
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• pp.19-31
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• 2008

Video image processing systems(VIPS) offer numerous benefits to transportation models and applications, due to their ability to monitor traffic in real time. VIPS based on wide-area detection, i.e., multi-lane surveillance algorithm provide traffic parameters with single camera such as flow and velocity, as well as occupancy and density. However, most current commercial VIPS utilize a tripwire detection algorithm that examines image intensity changes in the detection regions to indicate vehicle presence and passage, i.e., they do not identify individual vehicles as unique targets. If VIPS are developed to track individual vehicles and thus trace vehicle trajectories, many existing transportation models will benefit from more detailed information of individual vehicles. Furthermore, additional information obtained from the vehicle trajectories will improve incident detection by identifying lane change maneuvers and acceleration/deceleration patterns. The objective of this research was to relate traffic safety to VIPS tracking and this paper has developed a computer vision system of monitoring individual vehicle trajectories based on image processing, and offer the detailed information, for example, volumes, speed, and occupancy rate as well as traffic information via tripwire image detectors. Also the developed system has been verified by comparing with commercial VIP detectors.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.4
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• pp.400-405
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• 2005

This paper analyzes the performance of a face recognition algorithm using the eigenfaces method. In the absence of robust personal recognition schemes, a biometric recognition system has essentially researched to improve their shortcomings. A face recognition system in biometries is widely researched in the field of computer vision and pattern recognition, since it is possible to comprehend intuitively our faces. The proposed system projects facial images onto a feature space that effectively expresses the significant variations among known facial images. The significant features are known as 'eigenfaces', because they are the eigenvectors(principal components) of the set of faces. The projection operation characterizes an individual face by a weighted sum of the eigenface features, and to recognize a particular face it is necessary only to compare these weights to those of known individuals. In order to analyze the performance of the system, we develop a face recognition system by using Harvard database in Harvard Robotics Laboratory. We present the recognition rate according to variations on the lighting condition, numbers of the employed eigenfaces, and existence of a pre-processing step. Finally, we construct a rejection curve in order to investigate the practicability of the recognition method using the eigenfaces.