• Journal of Korean Neurosurgical Society
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• v.46 no.3
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• pp.232-238
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• 2009

Objective : Intracranial aneurysms are sometimes presented with visual symptoms by their rupture or direct compression of the optic nerve. It is because their prevalent sites are anatomically located close to the optic pathway. Anterior communicating artery is especially located in close proximity to optic nerve. Aneurysm arising in this area can produce visual symptoms according to their direction while the size is small. Clinical importance of visual symptoms presented by aneurysmal optic nerve compression is stressed in this study. Methods : Retrospective analysis of ruptured anterior communicating artery aneurysms compressing optic apparatus were carried out. Total 33 cases were enrolled in this study. Optic nerve compression of the aneurysms was confirmed by the surgical fields. Results : In 33 cases among 351 cases of ruptured anterior communicating artery aneurysms treated surgically, from 1991 to 2000, the dome of aneurysm was compressed in optic pathway. In some cases, aneurysm impacted into the optic nerve that deep hollowness was found when the aneurysm sac was removed during operation. Among 33 cases, 10 cases presented with preoperative visual symptoms, such as visual dimness (5), unilateral visual field defect (2) or unilateral visual loss (3), 20 cases had no visual symptoms. Visual symptoms could not be checked in 3 cases due to the poor mental state. In 6 cases among 20 cases having no visual symptoms, optic nerve was deeply compressed by the dome of aneurysm which was seen in the surgical field. Of 10 patients who had visual symptoms, 8 showed improvement in visual symptoms within 6 months after clipping of aneurysms. In 2 cases, the visual symptoms did not recover. Conclusion : Anterior communicating artery aneurysm can cause visual symptoms by compressing the optic nerve or direct rupture to the optic nerve with focal hematoma formation. We emphasize that cerebral vascular study is highly recommended to detect intracranial aneurysm before its rupture in the case of normal CT findings with visual symptoms and frequent headache.

• Journal of Industrial Technology
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• v.21 no.B
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• pp.67-74
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• 2001

According as the patterns of PCB (Printed Circuit Board) become denser and complicated, quality and accuracy of PCB influence the performance of final product. It's attempted to obtain trust of 100% about all of parts. Because human inspection in mass-production manufacturing facilities are both time-consuming and very expensive, the automation of visual inspection has been attempted for many years. Thus, automatic visual inspection of PCB is required. In this paper, we used an algorithm which compares the reference PCB patterns and the input PCB patterns are separated an object and a scene by filtering and edge detection. And than compare two image using pattern matching algorithm. We suggest an defect inspection algorithm in PCB pattern, to be satisfied low cost, high speed, high performance and flexibility on the basis of $640{\times}480$ binary pattern.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.580-582
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• 2004

As the demand of large and high-resolution display panels is increased, the black light units (BLU) of the display devices play an important roles. In this study we'll deal with various defects of BLUs. Patterns of defects can be classified by the scratches, the non-uniform misprinting for the diffused reflection, the surface stains, spots and etc. Due to these distorted patterns the high-resolution and high-precision could be impeded. We'll propose the visual inspection system to detect various defects by pattern-matching.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2002.05a
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• pp.16-19
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• 2002

Random and/or periodic defects occur in all spun yarns. These irregularities can often lead to defects in finished fabric. Yarn evenness tests are used to obtain statistical data about yarn properties, such as CV%, which is useful in comparing several sets of similar data that differ in mean value but may have some commonality in relative variation. Although this statistical data is helpful in determining relative yarn Quality, accurate predictions of how the yarn will appear in fabric form are still difficult to obtain. As an promising alterative, wavelet analysis has been employed to localize yam defect so as to predict the visual qualifies of the fabrics.

• Science of Emotion and Sensibility
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• v.5 no.3
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• pp.35-38
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• 2002

Random and/or periodic defects occur in all spun yarns. These irregularities can often lead to defects in finished fabric. Yarn evenness tests are used to obtain statistical data about yarn properties, such as CV%, which is useful in comparing several sets of similar data that differ in mean value but may have some commonality in relative variation. Although this statistical data is helpful in determining relative yam quality, accurate predictions of how the yarn will appear in fabric form are still difficult to obtain. As an promising alterative, wavelet analysis has been employed to localize yarn defect so as to predict the visual qualities of the fabrics.

• Proceedings of the KIEE Conference
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• 1987.11a
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• pp.311-315
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• 1987

This paper describes the automatic visual inspect ion system of fabric defects based on pattern recognition techniques. To extract features for detection of fabric defects, four different techniques such as SGLDM. GCM, decorrelation method, and Laws' texture measure were investigated. From results of computer simulation, it has been found that GCM and decorrelation techniques provide good features. By employing a simple statistical pattern recognition technique, theaccuracy of classification of defect and nondefect was more than 90%. Some experimental results arm also presented.

• Proceedings of the KIEE Conference
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• 1988.07a
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• pp.959-962
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• 1988

This paper describes an automatic visual inspection system for fabric defects based on pattern recognition techniques. The inspection for fabric defects can be separated into three sequences of operations which are the detection of fabric defects[1], the classification of figures of fabric defects, and the classification of fabric defects. Comparing projections of defect-detected images with the predefined complex, the classification accuracy of figures of fabric defects was found to be 95.3 percent. Employing the Bayes classifier using cluster shade in SGLDM and variance in decorrelation method as features, the classification accuracy of regional figure defects was found to be 82.4 percent. Finally, some experimental results for line and dispersed figures of fabric defects are included.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1940-1944
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• 2004

• Proceedings of the IEEK Conference
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• 1998.06a
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• pp.769-772
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• 1998

This paper present an efficient techniques for visual inspection of taped electronic parts, suitable for real time implementation. The main environments of developed system are IBM-compatible personal computer, frame grabber, digital input-output board. It is connected to the programmable logic controller unit of the taping machine in real time. Using a queuing structure, operator or extractor machine can remove easily the defect one from production line. Also, we design a new illumination system for sacquring shape and subface features of object. Therefore, it redue pre-processing step and processing time.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1994.10a
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• pp.796-800
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• 1994

결함검사에 영상처리기술을 적용할때, 데이타량 때문에 많은 처리시간과 판정의 오류들이 발생할수 있다. 본 연구에서는 검사를 해야할 화상을 푸리에 변환과 특징 값 추출을 통해서 판정의 오류를 줄이고 처리해야할 데이타량을 줄일 수 있음을 보여준다. 검사 알고리즘은 두단계의 비교과정을 거치면서 설정된 기준치를 넘는 경우가 생기면 결함으로 판단을 하는 단순한 튿정값의 비교에 의해서 수행이 될 수 있음을 보여준다.