• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.9 no.2
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• pp.196-204
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• 2016

World Health Organization reported that heart-related diseases such as coronary artery stenoses show the highest occurrence rate which may cause heart attack. Using Computed Tomography angiography images will allow radiologists to detect and have intervention by creating 3D roadmapping of the vessels. However, it is often complex and difficult do reconstruct 3D vessel which causes very large amount of time and previous researches were studied to segment vessels more accurate automatically. Therefore, in this paper, Region Competition, Geodesic Active Contour (GAC), Multi-atlas based segmentation and Active Shape Model algorithms were applied to segment aortic root from CTA images and the results were analyzed by using mean Hausdorff distance, volume to volume measure, computational time, user-interaction and coronary ostium detection rate. As a result, Extracted 3D aortic model using GAC showed the highest accuracy but also showed highest user-interaction results. Therefore, it is important to improve automatic segmentation algorithm in future

• Journal of the Korean Society for information Management
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• v.29 no.4
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• pp.191-208
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• 2012

Two fundamental aspects of speech summary generation are the extraction of key speech content and the style of presentation of the extracted speech synopses. We first investigated whether acoustic features (speaking rate, pitch pattern, and intensity) are equally important and, if not, which one can be effectively modeled to compute the significance of segments for lecture summarization. As a result, we found that the intensity (that is, difference between max DB and min DB) is the most efficient factor for speech summarization. We evaluated the intensity-based method of using the difference between max-DB and min-DB by comparing it to the keyword-based method in terms of which method produces better speech summaries and of how similar weight values assigned to segments by two methods are. Then, we investigated the way to present speech summaries to the viewers. As such, for speech summarization, we suggested how to extract key segments from a speech video efficiently using acoustic features and then present the extracted segments to the viewers.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.4
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• pp.403-411
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• 2014

In this paper, an algorithm of extracting the features of home appliances and automatically building a database to identify home appliances is designed and presented. For the verification, a software library supporting this algorithm is implemented and added to an power management system server, which was already implemented to support real-time monitoring of home appliances' power consumption status and controlling their power. The implemented system consists of a system server and clients, each of which measures the power consumed by a home appliance plugged in it and transmits the information to the server in real-time over a wireless network. Through experiments, it is verified that it is possible to identify any home appliance connected to a specific client.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.5
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• pp.359-363
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• 2017

The LED (Liquid Emitting Diode) frame device is a big part of the representative display industry in Korea. LED is an essential part for TV, monitor, notebook, and mobile phone. In Japan, Taiwan, China and other countries, investment in LEDs has been strengthened, and productivity has become an important issue. However, as the size of the parts becomes smaller, the inconsistent inspection by the human eye becomes a problem of reliability, so that the automatic inspection process becomes an essential issue in the field of LED module inspection. In this paper, we investigate defects in visual inspection process using computer vision technology. The inspection of the LED frame is made quickly and accurately, thereby improving the efficiency of the process and shortening the inspection time. As a result of applying the inspection system to the field, we confirmed that it is possible to inspect quickly and accurately.

• Korean Journal of Cognitive Science
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• v.22 no.1
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• pp.1-18
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• 2011

With rapid spread of handheld devices like cellular or smart phones, a short message service (SMS) comes on the public as a communication means. SMS is very cheap and can be easily written down on the storage in order not to forget it, hence it is widely used to inform schedules (time and place). In this paper, we develop a system for automatically registering schedules extracted from SMS text messages. SMS text messages are very short and concise, but include a lot of Internet words like slangs and abbreviations. These have made it difficult to extract information on schedules from them. Also handheld devices have some limitations on computing power and storage and then applying general natural language processing modules like morphological analysis to the devices are somewhat hard. To relax these burdens, we extract schedule informations from SMS messages using machine learning methods like condition random field (CRF) without using any language processing modules and register the informations on the schedule management system of handheld devices. Our proposed automatic schedule registration system has implemented on Samsung Omnia phone for experiments.

• Journal of KIISE:Software and Applications
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• v.29 no.3
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• pp.202-210
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• 2002

During the last few years, the extraction and reconstruction of the blood vessels in the medical image has been actively researched and the analysis for the retinal vessel structure has provided important information for diagnosis and remedy of the retinopathy patients. In this research, we propose the algorithm that tracks automatically the entire retinal vessel in retinal image acquired by the ICG(IndoCyanine Green) technology. This algorithm extracts contours and centers by estimating the local maxima and processing directions and detects bifurcations and junctions by comparing direction components of the local maxima from the gradient magnitude profile of each blood vessel. We present experimental results that the entire blood vessel is automatically reconstructed and is excellent in accuracy and connectivity after applying our algorithm to the ICG retinal images of patients.

• Journal of KIISE:Software and Applications
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• v.33 no.4
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• pp.418-426
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• 2006

This paper proposes a robust and efficient segmentation method for a cardiac ultrasound image taken from a probe inserted into the heart in surgery. The method consists of three steps: initial boundary extraction, whole boundary modification using confidence competition, and local boundary modification using the rolling spoke method. Firstly, the initial boundary is extracted with threshold regions along the global spokes emitted from the center of an ultrasound probe. Secondly, high confidence boundary edges are detected along the global spokes by competing among initial boundary candidate and new candidates achieved by edge and appearance information. finally, the boundary is modified by rolling local spokes along concave regions that are difficult to extract using the global spokes. The proposed method produces promising segmentation results for the ultrasound cardiac images acquired during surgery.

• The KIPS Transactions:PartB
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• v.19B no.1
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• pp.19-26
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• 2012

The rapid increase of information imposes new demands of content management. The purpose of automatic audio segmentation and classification is to meet the rising need for efficient content management. With this reason, this paper proposes a high-accuracy algorithm that segments audio signals and classifies them into different classes such as speech, music, silence, and environment sounds. The proposed algorithm utilizes support vector machine (SVM) to detect audio-cuts, which are boundaries between different kinds of sounds using the parameter sequence. We then extract feature vectors that are composed of statistical data and they are used as an input of fuzzy c-means (FCM) classifier to partition audio-segments into different classes. To evaluate segmentation and classification performance of the proposed SVM-FCM based algorithm, we consider precision and recall rates for segmentation and classification accuracy for classification. Furthermore, we compare the proposed algorithm with other methods including binary and FCM classifiers in terms of segmentation performance. Experimental results show that the proposed algorithm outperforms other methods in both precision and recall rates.

• The KIPS Transactions:PartB
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• v.14B no.7
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• pp.481-492
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• 2007

In this paper, a method for automatic indexing of contents to reduce effort for inputting paper information and constructing index is considered. Existing document analysis methods can't analyse various table of contents of journal paper formats efficiently because they have many exceptions. In this paper, various contents formats for journals, which have different features from those for general documents, are analysed and described. The principal elements that we want to represent are titles, authors, and pages for each papers. Thus, the three principal elements are modeled according to the order of their arrangement, and their features are extracted. And a table of content recognition system of journal is implemented, based on the proposed modeling method. The accuracy of exact extraction ratio of 91.5% on title, author, and page type on 660 published papers of various journals is obtained.

• The KIPS Transactions:PartB
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• v.17B no.4
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• pp.263-274
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• 2010

To provide context-aware music recommendation service, first of all, we need to catch music mood that a user prefers depending on his situation or context. Among various music characteristics, music mood has a close relation with people‘s emotion. Based on this relationship, some researchers have studied on music mood detection, where they manually select a representative segment of music and classify its mood. Although such approaches show good performance on music mood classification, it's difficult to apply them to new music due to the manual intervention. Moreover, it is more difficult to detect music mood because the mood usually varies with time. To cope with these problems, this paper presents an automatic method to classify the music mood. First, a whole music is segmented into several groups that have similar characteristics by structural information. Then, the mood of each segments is detected, where each individual's preference on mood is modelled by regression based on Thayer's two-dimensional mood model. Experimental results show that the proposed method achieves 80% or higher accuracy.