• Journal of IKEEE
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• v.15 no.4
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• pp.274-280
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• 2011

The implementation of the recognition system of a vehicle license plate and the Linux OS environment which is built in SoC Embedded system and its test result are presented in this paper. In order to recognize a vehicle license plate, each character has to be extracted from the whole image of a license plate and the extracted image is revised for the template matching. Labeling technique and numerical features are used to detect the vehicle license plate. Each character in the license plate has coordinates. The extracted image is revised by comparison of the numerical coordinates and recognized through template matching method. The experimental results show that the license plate detection rate is 96%, and a character recognition rate is 73%, and a number recognition rate is 97% for about 300 license plate images. The average time of the recognition in the embedded board is 0.66 sec.

• Land and Housing Review
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• v.14 no.4
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• pp.95-102
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• 2023

Numerous investigations have explored the integration of machine leaning algorithms with high-resolution drone image for object detection in urban settings. However, a prevalent limitation in vehicle extraction studies involves the reliance on bounding boxes rather than instance segmentation. This limitation hinders the precise determination of vehicle direction and exact boundaries. Instance segmentation, while providing detailed object boundaries, necessitates labour intensive labelling for individual objects, prompting the need for research on automating unsupervised instance segmentation in vehicle extraction. In this study, a novel approach was proposed for vehicle extraction utilizing unsupervised SVM classification applied to vehicle bounding boxes in drone images. The method aims to address the challenges associated with bounding box-based approaches and provide a more accurate representation of vehicle boundaries. The study showed promising results, demonstrating an 89% accuracy in vehicle extraction. Notably, the proposed technique proved effective even when dealing with significant variations in spectral characteristics within the vehicles. This research contributes to advancing the field by offering a viable solution for automatic and unsupervised instance segmentation in the context of vehicle extraction from image.

• The KIPS Transactions:PartD
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• v.15D no.1
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• pp.1-14
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• 2008

This paper is on query processing in the mobile devices where memory capacity is limited. In case that a query against a large volume of XML data is processed in such a mobile device, techniques of fragmenting the XML data into chunks and of streaming and processing them are required. Such techniques make it possible to process queries without materializing the XML data in its entirety. The previous schemes such as XFrag[4], XFPro[5], XFLab[6] are not scalable with respect to the increase of the size of the XML data because they lack proper memory management capability. After some information on XML fragments necessary for query processing is stored, it is not deleted even after it becomes of no use. As such, when the XML fragments are dynamically generated and infinitely streamed, there could be no guarantee of normal completion of query processing. In this paper, we address scalability of query processing over dynamic XML fragment stream, proposing techniques of deleting information on XML fragments accumulated during query processing in order to extend the previous schemes. The performance experiments through implementation showed that our extended schemes considerably outperformed the previous ones in memory efficiency and scalability with respect to the size of the XML data.

• Journal of Internet Computing and Services
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• v.18 no.1
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• pp.65-75
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• 2017

The Detection Model is the model to find the result of a certain purpose using artificial intelligent, data mining, intelligent algorithms In Cyber Security, it usually uses to detect intrusion, malwares, cyber incident, and attacks etc. There are an amount of unlabeled data that are collected in a real environment such as security data. Since the most of data are not defined the class labels, it is difficult to know type of data. Therefore, the label determination process is required to detect and analysis with accuracy. In this paper, we proposed a KDFL(K-means and D-S Fusion based Labeling) method using D-S inference and k-means(unsupervised) algorithms to decide label of data records by fusion, and a detection model architecture using a proposed labeling method. A proposed method has shown better performance on detection rate, accuracy, F1-measure index than other methods. In addition, since it has shown the improved results in error rate, we have verified good performance of our proposed method.

• Journal of KIISE:Software and Applications
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• v.33 no.11
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• pp.942-952
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• 2006

We propose an automatic segmentation method for identifying pulmonary structures using gray-level information of chest CT images. Our method consists of following five steps. First, to segment pulmonary structures based on the difference of gray-level value, we select the threshold using optimal thresholding. Second, we separate the thorax from the background air and then the lungs and airways from the thorax by applying the inverse operation of 2D region growing in chest CT images. To eliminate non-pulmonary structures which has similar intensities with the lungs, we use 3D connected component labeling. Third, we segment the trachea and left and right mainstem bronchi using 3D branch-based region growing in chest CT images. Fourth, we can obtain accurate lung boundaries by subtracting the result of third step from the result of second step. Finally, we select the threshold in accordance with histogram analysis and then segment radio-dense pulmonary vessels by applying gray-level thresholding to the result of the second step. To evaluate the accuracy of proposed method, we make a visual inspection of segmentation result of lungs, airways and pulmonary vessels. We compare the result of the conventional region growing with the result of proposed 3D branch-based region growing. Experimental results show that our proposed method extracts lung boundaries, airways, and pulmonary vessels automatically and accurately.

• Journal of the Korea Convergence Society
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• v.8 no.8
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• pp.33-40
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• 2017

In this paper, we propose a text extraction method by pattern analysis on contour for effective text detection in image. Text extraction algorithms using edge based methods show good performance in images with simple backgrounds, The images of complex background has a poor performance shortcomings. The proposed method simplifies the color of the image by using K-means clustering in the preprocessing process to detect the character region in the image. Enhance the boundaries of the object through the High pass filter to improve the inaccuracy of the boundary of the object in the color simplification process. Then, by using the difference between the expansion and erosion of the morphology technique, the edges of the object is detected, and the character candidate region is discriminated by analyzing the pattern of the contour portion of the acquired region to remove the unnecessary region (picture, background). As a final result, we have shown that the characters included in the candidate character region are extracted by removing unnecessary regions.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.5
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• pp.734-739
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• 2018

Fingerprint recognition and iris recognition, which are one of the biometric methods, are easily influenced by external factors such as sunlight. Recently, finger vein recognition is used as a method utilizing internal features. However, for accurate finger vein recognition, it is important to clearly separate vein and background regions. However, it is difficult to separate the vein region and background region due to the abnormalized illumination, and a method of separating the vein region and the background region after normalized the illumination of the input image has been proposed. In this paper, we proposed a method to enhance the quality improvement and improve the processing time compared to the existing finger vein recognition system binarization and labeling method of the image including the image stretching process based on the existing illumination normalization method.

• Journal of Advanced Navigation Technology
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• v.20 no.5
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• pp.496-502
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• 2016

In this paper, we proposed and implemented a GUI-based system to recognize and record measured values of digital instruments in the industrial site through image processing. Unlike the existing vehicle license plate recognition system, the measured values of the measuring instrument are displayed on the LCD screen as digital numbers. So, the proposed system considers the decimal point, a negative sign, light reflected by LCD protective glass, and various disturbance factors. We used blob-labeling technique to recognize the numbers displayed on the LCD screen, the recognized number images were determined as certain numbers through the template matching, and recognized values were recorded in the storage device with measurement time. Therefore, the proposed system in this paper would reduce the burden of writing when recording the measured values of the inside/outside diameter or height of the product in the industrial site, so effective and errorless process management in production process is possible by preventing errors in recording measurements when written by hand.

• Journal of KIISE:Databases
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• v.35 no.2
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• pp.112-124
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• 2008

RDF and OWL are the primary base technologies for implementing Semantic Web. Recently, many researches related with them, or applying them into the other application domains, have been introduced. However, relatively little work has been done for securing the RDF and OWL data. In this article, we briefly introduce an RDF triple based model for specifying RDF access authorization related with RDF security. Next, to efficiently find the authorization conflict by RDF inference, we introduce a method using prime number graph labeling in detail. The problem of authorization conflict by RDF inference is that although the lower concept is permitted to be accessed, it can be inaccessible due to the disapproval for the upper concept. Because by the RDF inference, the lower concept can be interpreted into the upper concept. Some experimental results show that the proposed method using the prime number graph labeling has better performance than the existing simple method for the detection of the authorization conflict.

• Journal of KIISE:Computer Systems and Theory
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• v.33 no.4
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• pp.231-241
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• 2006

Creating controllable, responsive avatars is an important problem in computer games and virtual environments. Recently, large collections of motion capture data have been exploited for increased realism in avatar animation and control. Large motion sets have the advantage of accommodating a broad variety of natural human motion. However, when a motion set is large, the time required to identify an appropriate sequence of motions is the bottleneck for achieving interactive avatar control. In this paper, we present a novel method for training avatar behaviors from unlabelled motion data in order to animate and control avatars at minimal runtime cost. Based on machine learning technique, called Q-teaming, our training method allows the avatar to learn how to act in any given situation through trial-and-error interactions with a dynamic environment. We demonstrate the effectiveness of our approach through examples that include avatars interacting with each other and with the user.