• Journal of the Korea Convergence Society
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• v.8 no.11
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• pp.43-48
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• 2017

Semiconductors are leading the development of industrial technology, leading to miniaturization and weight reduction of electronic products as a leading technology, we are dragging the electronic industry market Especially, the semiconductor manufacturing process is composed of highly accurate and complicated processes, and effective production is required Recently, a vision system combining a computer and a camera is utilized for defect detection In addition, the demand for a system for measuring the shape of a fine pattern processed by a special process is rapidly increasing. In this paper, we propose a vision algorithm using mask image to detect scratch defect of semiconductor pockage. When applied to the manufacturing process of semiconductor packages via the proposed system, it is expected that production management can be facilitated, and efficiency of production will be enhanced by failure judgment of high-speed packages.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.6
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• pp.285-294
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• 2004

Median Filters in the Signal Processing have been most widely used and have demonstrated the most strongest effects. This paper proposes the Adaptive Median Filters by using noise detection. The basic algorithm of the proposed filters is to determine whether noise or not by the each noise judgement standards, and then take the Median Filter if it satisfies the conditions as a result of judgement and returns to the original image(No Filters) if not. This paper presented Noise Detection by Local Variance and Local Central Variance for noise judgement, compared and analyzed the features and performance of existing [5]∼[10] Filters. Filter improved on the result of executing the existing filters at the same condition and showed the effects over that when it was judged with naked eyes. Accordingly, the Adaptive Median Filters by Local Variance and Local Central Variance was proven to have reinforced edge preservation ability and have the strong features for removing the Impulse Noise of the Median Filter.

• Journal of Digital Contents Society
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• v.18 no.8
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• pp.1635-1640
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• 2017

In this paper, a hierarchical image tree model for weather classification is defined in a single outdoor image, and a weather classification algorithm using image intensity and k-mean segmentation image is proposed. In the first level of the hierarchical image tree model, the indoor and outdoor images are distinguished. Whether the outdoor image is daytime, night, or sunrise/sunset image is judged using the intensity and the k-means segmentation image at the second level. In the last level, if it is classified as daytime image at the second level, it is finally estimated whether it is sunny or foggy image based on edge map and fog rate. Some experiments are conducted so as to verify the weather classification, and as a result, the proposed method shows that weather features are effectively detected in a given image.

• Smart Media Journal
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• v.6 no.3
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• pp.15-20
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• 2017

Recently, the automotive industry has been studied about driver assistance systems for helping drivers to drive their cars easily by integrating them with the IT technology. This study suggests a method of detecting lanes, robust to road condition changes and applicable to lane departure warning and autonomous vehicles mode. The proposed method uses the method of detecting candidate areas by using the Gaussian filter and by determining the Otsu threshold value and edge. Moreover, the proposed method uses lane gradient and width information through the Hough transform to detect lanes. The method uses road lane information detected before to detect dashed lines as well as solid lines, calculates routes in which the lanes will be located in the next frame to draw virtual lanes. The proposed algorithm was identified to be able to detect lanes in both dashed- and solid-line situations, and implement real-time processing where applied to Raspberry Pi 2 which is open source hardware.

• Journal of the Korea Society of Computer and Information
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• v.14 no.5
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• pp.45-54
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• 2009

In this paper, we propose an adult images detection algorithm using a mean intensity filter and an improved 2D Hough Transform. This paper is composed of three major steps including a training step, a recognition step, and a verification step. The training step generates a mean nipple variance filter that will be used for detecting nipple candidate regions in the recognition step. To make the mean variance filter, we converts an input color image into a gray scale image and normalize it, and make an average intensity filter for nipple areas. The recognition step first extracts edge images and finds connected components, and decides nipple candidate regions by considering the ratio of width and height of a connected component. It then decides final nipple candidates by calculating the similarity between the learned nipple average intensity filter and the nipple candidate areas. Also, it detects breast lines of an input image through the improved 2D Hough transform. The verification step detects breast areas and identifies adult images by considering the relations between nipple candidate regions and locations of breast lines.

• Journal of Korea Water Resources Association
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• v.38 no.10 s.159
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• pp.841-849
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• 2005

Two-phase flows, e.g. sediment-laden flow and bubbly flow, have two different flow profiles; flow velocity and sediment velocity. To measure velocity distributions of two-phase flows, it is necessary to use sophisticated instruments which can separate velocity profiles of two-phases. For bubbly flows, PIV (Particle Image Velocimetry) or PTV (Particle Tracking Velocimetry) has given fairly good velocity profiles of two-phases. However, for sediment-laden flows, the applications of PIV or PTV has not been so successful, because the sediment particles introduced to the flow kept the images from being analyzed. A new algorithm, which consists of several image analysis methods, is proposed to analyze sediment-laden flows. For detection algorithm, threshold method, edge detection method, and thinning method are adapted, and for finding matching pair PIV and PTV routines are combined. The proposed method can (1) detect sediment particles with irregular boundaries, (2) remove reflected images and scattered images, and (3) discriminate tracer particles from reflected images of sediment particles.

• The KIPS Transactions:PartA
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• v.16A no.6
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• pp.453-462
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• 2009

Studies on software plagiarism detection, prevention and judgement have become widespread due to the growing of interest and importance for the protection and authentication of software intellectual property. Many previous studies focused on comparing all pairs of submitted codes by using attribute counting, token pattern, program parse tree, and similarity measuring algorithm. It is important to provide a clear-cut model for distinguishing plagiarism and collaboration. This paper proposes a source code clustering algorithm using a probability model on extreme value distribution. First, we propose an asymmetric distance measure pdist($P_a$, $P_b$) to measure the similarity of $P_a$ and $P_b$ Then, we construct the Plagiarism Direction Graph (PDG) for a given program set using pdist($P_a$, $P_b$) as edge weights. And, we transform the PDG into a Gumbel Distance Graph (GDG) model, since we found that the pdist($P_a$, $P_b$) score distribution is similar to a well-known Gumbel distribution. Second, we newly define pseudo-plagiarism which is a sort of virtual plagiarism forced by a very strong functional requirement in the specification. We conducted experiments with 18 groups of programs (more than 700 source codes) collected from the ICPC (International Collegiate Programming Contest) and KOI (Korean Olympiad for Informatics) programming contests. The experiments showed that most plagiarized codes could be detected with high sensitivity and that our algorithm successfully separated real plagiarism from pseudo plagiarism.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.6
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• pp.1201-1208
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• 2020

A mass of seawater with similar properties in the ocean is called a water mass, and the front is a sea area where two masses of different properties meet. The gradient algorithm is a method of extracting where the sea water temperature pixel changes rapidly assuming that the slope is large, and the place with the large slope is assumed to be a front. This method is able to process large amounts of satellite data at once. Therefore, in this study, we tried to find the front lines in the sea area around the Korean Peninsula by using a gradient algorithm. The study data used gridded sea surface temperature satellite data. The resolution was 1/4°, and the monthly average data from January 1993 to December 2018 were used. There were major five fronts representatively, China Coastal Front, South Sea Coastal Front, Kuroshio Front/ Kuroshio Extension Front, Subpolar Front and the Subarctic Front. As a result of comparing the distribution of front by season, more types of front were distributed in winter and spring than in summer and autumn, and the distribution range was wider.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.2 s.308
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• pp.1-11
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• 2006

In this paper, we propose the novel video retrieval algorithm using non-parametric based motion classification in the shot-based video indexing structure. The proposed system firstly gets the key frame and motion information from each shot segmented by scene change detection method, and then extracts visual features and non-parametric based motion information from them. Finally, we construct real-time retrieval system supporting similarity comparison of these spatio-temporal features. After the normalized motion vector fields is created from MPEG compressed stream, the extraction of non-parametric based motion feature is effectively achieved by discretizing each normalized motion vectors into various angle bins, and considering a mean, a variance, and a direction of these bins. We use the edge-based spatial descriptor to extract the visual feature in key frames. Experimental evidence shows that our algorithm outperforms other video retrieval methods for image indexing and retrieval. To index the feature vectors, we use R*-tree structures.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1405-1408
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• 2005

An embedded system has been applied to many fields including households and industrial sites. In the past, user interface products with simple functions were commercialized .but now user demands are increasing and the system has more various applicable fields due to a high penetration rate of the Internet. Therefore, the demand for embedded system is tend to rise In this paper, we Implementation of an embedded system for image tracking. This system is used a fixed IP for the reliable server operation on TCP/IP networks. A real time broadcasting of video image on the internet was developed by using an USB camera on the embedded Linux system. The digital camera is connected at the USB host port of the embedded board. all input images from the video camera is continuously stored as a compressed JPEG file in a directory at the Linux web-server. And each frame image data from web camera is compared for measurement of displacement Vector. That used Block matching algorithm and edge detection algorithm for past speed. And the displacement vector is used at pan/tilt motor control through RS232 serial cable. The embedded board utilized the S3C2410 MPU Which used the ARM 920T core form Samsung. The operating system was ported to embedded Linux kernel and mounted of root file system. And the stored images are sent to the client PC through the web browser. It used the network function of Linux and it developed a program with protocol of the TCP/IP.