• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.183-185
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• 2021

Detecting bacteria in blood could be an important research area in medicine and computer vision. In this paper, we propose a method for detecting bacteria in blood from 366 darkfield microscopy images acquired at Kaggle. Generate a training dataset through preprocessing and data augmentation using image processing techniques, and define a deep learning model for learning it. As a result of the experiment, it was confirmed that the proposed deep learning model effectively detects red blood cells and bacteria in darkfield microscopy images. In this paper, we learned using a relatively simple model, but it seems that more accurate results can be obtained by using a deeper model.

• Journal of the Korea Society of Computer and Information
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• v.19 no.1
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• pp.9-21
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• 2014

In this paper, we propose the auto correction technique of photography composition by which the eye line is concentrated and the stable image of the structure can be obtained in case the general user takes a picture. Because the general user photographs in most case without background knowledge about the composition of the photo, the subject location is not appropriate and the unstable composition is contrasted with the stable composition of pictures which the experts take. Therefore, we provide not the method processing the image after photographing, but he method presenting automatically the stable composition when the general users take a photograph. The proposed method analyze the subject through Saliency Map, Image Segmentation, Edge Detection, etc. and outputs the subject at the location where the stable composition can be comprised along with the guideline of the Rule of Thirds. The experimental result shows that the good composition was presented to the user automatically.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.5
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• pp.287-294
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• 2013

Binary connected-component labeling is widely used in the fields of the image processing and the computer vision. Many kinds of labeling techniques have been developed, and two-scan is known as the fastest method among them. Traditionally pixel-based scan masks have been used for the first stage of the two-scan. Recently, block-based labeling techniques were introduced by C. Grana et. al. and L. He et. al. They are faster than pixel-based labeling methods. In this paper, we propose a new binary connected-component labeling technique with block-based labels and a pixel-based scan mask. The experimental results with various images show that the proposed method is faster than the He's which is known as the fastest method currently. The amount of performance enhancement is averagely from 3.9% to 22.4% according to the sort of the images.

• Journal of Digital Contents Society
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• v.13 no.2
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• pp.255-262
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• 2012

Human motion tracking algorithm is receiving attention from many research areas, such as human computer interaction, video conference, surveillance analysis, and game or entertainment applications. Over the last decade, various tracking technologies for each application have been demonstrated and refined among them such of real time computer vision and image processing, advanced man-machine interface, and so on. In this paper, we introduce cost-effective and real-time human motion tracking algorithms based on depth image 3D point matching with a given superellipsoid body representation. The body representative model is made by using parametric volume modeling method based on superellipsoid and consists of 18 articulated joints. For more accurate estimation, we exploit initial inverse kinematic solution with classified body parts' information, and then, the initial pose is modified to more accurate pose by using 3D point matching algorithm.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.7
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• pp.962-970
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• 2021

The cause of the majority of vehicle accidents is a safety issue due to the driver's inattention, such as drowsy driving. A forward collision warning system (FCWS) can significantly reduce the number and severity of accidents by detecting the risk of collision with vehicles in front and providing an advanced warning signal to the driver. This paper describes a low power embedded system based FCWS for safety. The algorithm computes time to collision (TTC) through detection, tracking, distance calculation for the vehicle ahead and current vehicle speed information with a single camera. Additionally, in order to operate in real time even in a low-performance embedded system, an optimization technique in the program with high and low levels will be introduced. The system has been tested through the driving video of the vehicle in the embedded system. As a result of using the optimization technique, the execution time was about 170 times faster than that when using the previous non-optimized process.

• The Transactions of the Korea Information Processing Society
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• v.2 no.1
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• pp.23-33
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• 1995

This paper describes a visual programming tool which provides novice programmer as well expert with the abilites to capture real physical world of problem domain and to manipulate it user-friendly using icons and symbols. Therefore, novice can understand object-oriented features of C＋＋ incrementally and construct classes easily. For this, we introduce some visual metaphors which are displayed as tables. The tables can not only represent objects and classes, but also be considered themselves as icons. We have named these tables as table-icons. Three levels of table-icons(i.e., Super Table-Icons, Intermediate Table-Icons and Detailed Table-Icons) are proposed to follow up appropriate evolution of object-oriented concepts. Table-icons are not simple pictographs but are activated and expanded to table forms. And then, developer can inset necessary entities into table body and delete useless entities from it. These table-icons are applied to a diagramming technique, C＋＋gram［18］, which is suggested for designing and implementing C＋＋ programs.

• Journal of Broadcast Engineering
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• v.14 no.2
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• pp.253-264
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• 2009

This paper presents an algorithm, aims at practical applications, for the high speed processing and performance enhancement of lane detection base on vision processing system. As a preprocessing for high speed lane detection, the vanishing line estimation and the optimal extraction of region of interest for lane boundary (ROI-LB) can be processed to reduction of detection region in which high speed processing is enabled. Image feature information is extracted only in the ROI-LB. Road lane is extracted using a non-parametric model fitting and Hough transform within the ROI-LB. With simultaneous processing of noise reduction and edge enhancement using the Laplacian filter, the reliability of feature extraction can be increased for various road lane patterns. Since outliers of edge at each block can be removed with clustering of edge orientation for each block within the ROI-LB, the performance of lane detection can be greatly improved. The various real road experimental results are presented to evaluate the effectiveness of the proposed method.

• The KIPS Transactions:PartC
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• v.9C no.4
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• pp.481-488
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• 2002

Guarantee of the data integrity is important to the Virtual Private Network (VPN) which can be improved cost decreasing and effective work by applying on Internet as the private network. Thus, the integrity function in the VPN must be maintained and the security manager must be check it occasionally. In this paper we propose the VPN Integrity Evaluation System (VIES) which is collecting, and evaluating automatically the vulnerable data of VPN against current hacking mechanisms in information security system. And this VIES obtain to the results which have objectivity and fairness of evaluation by driving off the evaluation scenario based on Common Criteria (CC), and general users or non-specialist can utilize easy the security evaluation of organization.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.24 no.2
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• pp.217-226
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• 2006

Extraction and tracking objects are fundamental and important steps of the digital image processing and computer vision. Many algorithms about extracting and tracking objects have been developed. In this research, a method is suggested for tracking a moving object using a pair of CCD cameras and calculating the coordinate of the moving object. A 1/100 miniature of soccer field was made to apply the developed algorithms. After candidates were selected from the acquired images using the RGB value of a moving object (soccer ball), the object was extracted using its size (MBR size) among the candidates. And then, image coordinates of a moving object are obtained. The real-time position of a moving object is tracked in the boundary of the expected motion, which is determined by centering the moving object. The 3D position of a moving object can be obtained by conducting the relative orientation, absolute orientation, and space intersection of a pair of the CCD camera image.

• Journal of the Institute of Convergence Signal Processing
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• v.22 no.4
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• pp.156-163
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• 2021

Attention mechanisms have been used in deep learning-based computer vision systems, including single image super-resolution (SISR) networks. However, existing SISR networks with attention mechanism focused on real image super-resolution, so it is hard to know whether they are available for CG or VR images. In this paper, we attempt to apply a recent attention module, called balanced attention mechanism (BAM) module, to 12 state-of-the-art SISR networks, and then check whether the BAM module can achieve performance improvement in CG or VR image super-resolution. In our experiments, it has been confirmed that the performance improvement in CG or VR image super-resolution is limited and depends on data characteristics, size, and network type.