Doo Heon Song

An Empirical Model of the Game Software Development Processes

One of the hottest and fastest developing areas of computer software in Korea is computer game software. However, due to relatively young history and empirical nature of the area, there has not been any agreement or standardization of game software process design for its efficacy. In this paper, we propose a practical model of the game software development processes, which can be easily applicable to the traditional software development based on ISO12207 and/or RUP through the panel interview of game developers. We evaluate the model by on-line survey of another set of practical game developers and break down the results by game platform and game genre. And the results are quite promising

Nucleus Segmentation and Recognition of Uterine Cervical Pap-Smears

The classification of the background and cell areas is very important but difficult problem due to the ambiguity of boundaries. In this paper, the cell region is extracted from an image of uterine cervical cytodiagnosis using the region growing method. Segmented images from background and cell areas are binarized using a threshold value. And the 8-directional tracking algorithm for contour lines is applied to extract the cell area. Each extracted nucleus is transformed to the original RGB space. Then the K-Means clustering algorithm is employed to classify RGB pixels to the R, G, and B channels, respectively. Finally, the Hue information of nucleus is extracted from the HSI models that are transformed using the clustering values in R, G, and B channels. The fuzzy RBF Network is then applied to classify and identify the normal or abnormal nucleus. The result shows that the accuracy of our method is 80% overall and 66% in 5-class problem according to the Bethesda system.

extraction of Sternocleidomastoid and Longus Capitis/colli Muscle Using Cervical Vertebrae Ultrasound Images

Abstract:In this paper, we propose a method to extract sternocleidomastoid and longus capitis/colli automatically and measure the thickness of those muscles from cervical vertebrae ultrasound images. Extracting sternocleidomastoid is relatively easy but for longus capitis/colli case, due to the brig

Scratch Inspection of Spectacle Lens Based on Fuzzy Logic

In this paper, we propose an intelligent method to detect small scratches from eyeglasses using fuzzy logic. Inspecting scratches of eyeglasses largely depends on the native eye exam that may lead providing defected eyeglasses to customers at optician’s shop since those small scratches are often occurred in the process of transportation rather than that of production. Our method computes the possibility of ill-effect by scratch on glasses with membership degree of the scratch size and that of distance between the center and the scratch from candidate scratched areas extracted in the pre-processing phase. Our method is applied to CHEMI MID HL HM dioptric lenses in experiment and it is verified that the proposed method is sufficiently effective by real optician’s evaluation.

Real Time Recognition of Finger-Language Using Color Information and Fuzzy Clustering Algorithm

A finger language helping hearing impaired people in communication A sign language helping hearing impaired people in communication is not popular to ordinary healthy people. In this paper, we propose a method for real-time sign language recognition from a vision system using color information and fuzzy clustering system. We use YCbCr color model and canny mask to decide the position of hands and the boundary lines. After extracting regions of two hands by applying 8-directional contour tracking algorithm and morphological information, the system uses FCM in classifying sign language signals. In experiment, the proposed method is proven to be sufficiently efficient.

A Multiple Instance Learning Problem Approach Model to Anomaly Network Intrusion Detection

Even though mainly statistical methods have been used in anomaly network intrusion detection, to detect various attack types, machine learning based anomaly detection was introduced. Machine learning based anomaly detection started from research applying traditional learning algorithms of artificial intelligence to intrusion detection. However, detection rates of these methods are not satisfactory. Especially, high false positive and repeated alarms about the same attack are problems. The main reason for this is that one packet is used as a basic learning unit. Most attacks consist of more than one packet. In addition, an attack does not lead to a consecutive packet stream. Therefore, with grouping of related packets, a new approach of group-based learning and detection is needed. This type of approach is similar to that of multiple-instance problems in the artificial intelligence community, which cannot clearly classify one instance, but classification of a group is possible. We suggest group generation algorithm grouping related packets, and a learning algorithm based on a unit of such group. To verify the usefulness of the suggested algorithm, 1998 DARPA data was used and the results show that our approach is quite useful.

Developing an Intelligent Automatic Appendix Extraction Method from Ultrasonography Based on Fuzzy ART and Image Processing.

Ultrasound examination (US) does a key role in the diagnosis and management of the patients with clinically suspected appendicitis which is the most common abdominal surgical emergency. Among the various sonographic findings of appendicitis, outer diameter of the appendix is most important. Therefore, clear delineation of the appendix on US images is essential. In this paper, we propose a new intelligent method to extract appendix automatically from abdominal sonographic images as a basic building block of developing such an intelligent tool for medical practitioners. Knowing that the appendix is located at the lower organ area below the bottom fascia line, we conduct a series of image processing techniques to find the fascia line correctly. And then we apply fuzzy ART learning algorithm to the organ area in order to extract appendix accurately. The experiment verifies that the proposed method is highly accurate (successful in 38 out of 40 cases) in extracting appendix.

Moving cast shadow elimination algorithm using principal component analysis in vehicle surveillance video

Moving cast shadows on object distort figures which causes serious detection deficiency and analysis problems in ITS related applications. Thus, shadow removal plays an important role for robust object extraction from surveillance videos. In this paper, we propose an algorithm to eliminate moving cast shadow that uses features of color information about foreground and background figures. The significant information among the features of shadow, background and object is extracted by PCA transformation and tilting coordinates system. By appropriate analyses of the information, we found distributive characteristics of colors from the tilted PCA space. With this new color space, we can detect moving cast shadow and remove them effectively.

Semi-Dynamic Control of FCM Initialization for Automatic Extraction of Inflamed Appendix from Ultrasonography

Background: Current naked-eye examination of the ultrasound images for inflamed appendix has limitations due to its intrinsic operator subjectivity problem. Objective: In this paper, we propose a fully automatic intelligent method for extracting inflamed appendix from ultrasound images. Accurate and automatic extraction of inflamed appendix from ultrasonography is a major decision making resource of the diagnosis and management of suspected appendicitis. Methods: The proposed method uses Fuzzy C-means learning algorithm in pixel clustering with semi-dynamic control of initializing the number of clusters based on the intensity contrast dispersion of the input image. Thirty percent of the prepared ultrasonography samples are classified into four different groups based on their intensity contrast distribution and then different number of clusters are assigned to the images in accordance with such groups in Fuzzy C-means learning process. Results: In the experiment, the proposed system successfully extracts the target without human intervention in 82 of 85 cases (96.47% accuracy). The proposed method also shows that it can cover the false negative cases occurred previously that used self-organizing map as the learning engine. Conclusion: Such high level reliable correct extraction of inflamed appendix encourages to use the automatic extraction software in the diagnosis procedure of suspected acute appendicitis.

Gender Differences and Gender Stereotype in Play Style among Young Korean Gamers

흔히 여성 게이머는 아기자기하고 쉬운 게임을 선호하고 게임의 숙련도가 낮아 파티 구성 등에서 승리를 원하면 여성 게이머를 기피해야 한다고 믿는 경향이 있다. 문제는 이런 핑크 게임 이론은 요즘처럼 온라인 게임과 모바일 게임이 성행하기 전의 연구 결과들이라는 것이다. 최근의 외국 연구 결과들도 게임의 숙련 수준에서 성차가 전보다 현저히 줄어들었다는 보고도 있지만 한국의 남녀 게이머의 특성에 대한 연구는 2007년 이후 별로 이루어지고 있지 않다. 이에 본 연구는 10대 후반부터 30대에 이르는 88명의 남성 게이머와 151명의 여성 게이머를 대상으로 설문 조사를 한 바, 심각한 성 고정관념에 의한 행동과 인식의 괴리를 발견한다. 여전히 여러 부분에서 성차는 발견되지만 이전에 성차가 있던 부분이 약화되거나 없는 경우도 많았다. 게임 산업의 블루 오션인 여성 게이머의 증대를 위한 게임 기획은 이처럼 성별 게임 문화 차이를 제대로 인식하는 데서부터 출발해야 할 것이다.