• International Journal of Precision Engineering and Manufacturing
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• 제7권1호
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• pp.30-34
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• 2006

Recently, there has been an increasing demand for computer-vision-based inspection and/or measurement system as a part of factory automation equipment. In general, it is almost impossible to check the fault of all parts, coming from part-feeding system, with only manual inspection because of time limitation. Therefore, most of manual inspection is applied to specific samples, not all coming parts, and manual inspection neither guarantee consistent measuring accuracy nor decrease working time. Thus, in order to improve the measuring speed and accuracy of the inspection, a computer-aided measuring and analysis method is highly needed. In this paper, a computer-vision-based pipe inspection system is proposed, where the front and side-view profiles of three different kinds of pipes, coming from a forming line, are acquired by computer vision. And the edge detection is processed by using Laplace operator. To reduce the vision processing time, modified Hough transform is used with clustering method for straight line detection. And the center points and diameters of inner and outer circle are found to determine eccentricity of the parts. Also, an inspection system has been built so that the data and images of faulted parts are stored as files and transferred to the server.

• 한국컴퓨터정보학회논문지
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• 제19권12호
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• pp.11-19
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• 2014

본 논문에서는 데이터마이닝모듈과 정보융합모듈을 핵심구성요소로 가지는 지능형에이전트 시스템을 설계하고 다각도 정보를 융합하여 진단전문가시스템으로 활용할 수 있는 가능성을 제시한다. 데이터마이닝모듈에서는 퍼지신경망 OFUN-NET에 의하여 다각도의 데이터를 분석하고 퍼지 클러스터 정보를 지식베이스로 구축한다. 정보융합모듈과 응용모듈에서는 가능성정도로 제공되는 진단결과와 불확실 결정상태나 비대칭의 발견과 같은 전문가의 진단에 유용한 정보를 제공해 주고 있다. 또한 DDSM 벤치마크 데이터베이스로부터 획득한 디지털 유방 x선 영상의 BI-RADS 기반 특징데이터를 가지고 실험한 결과는 기존의 방법보다 높은 분류 정확도를 보여주면서 컴퓨터보조진단시스템으로서의 가능성을 보여주고 있다.

• Asian Pacific Journal of Cancer Prevention
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• 제17권11호
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• pp.4869-4873
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• 2016

Colonoscopy is currently the best technique available for the detection of colon cancer or colorectal polyps or other precursor lesions. Computer aided detection (CAD) is based on very complex pattern recognition. Local binary patterns (LBPs) are strong illumination invariant texture primitives. Histograms of binary patterns computed across regions are used to describe textures. Every pixel is contrasted relative to gray levels of neighbourhood pixels. In this study, colorectal polyp detection was performed with colonoscopy video frames, with classification via J48 and Fuzzy. Features such as color, discrete cosine transform (DCT) and LBP were used in confirming the superiority of the proposed method in colorectal polyp detection. The performance was better than with other current methods.

• Clinical Endoscopy
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• 제57권3호
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• pp.302-308
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• 2024

With incessant advances in information technology and its implications in all domains of our lives, artificial intelligence (AI) has emerged as a requirement for improved machine performance. This brings forth the query of how this can benefit endoscopists and improve both diagnostic and therapeutic endoscopy in each part of the gastrointestinal tract. Additionally, it also raises the question of the recent benefits and clinical usefulness of this new technology in daily endoscopic practice. There are two main categories of AI systems: computer-assisted detection (CADe) for lesion detection and computer-assisted diagnosis (CADx) for optical biopsy and lesion characterization. Quality assurance is the next step in the complete monitoring of high-quality colonoscopies. In all cases, computer-aided endoscopy is used, as the overall results rely on the physician. Video capsule endoscopy is a unique example in which a computer operates a device, stores multiple images, and performs an accurate diagnosis. While there are many expectations, we need to standardize and assess various software packages. It is important for healthcare providers to support this new development and make its use an obligation in daily clinical practice. In summary, AI represents a breakthrough in digestive endoscopy. Screening for gastric and colonic cancer detection should be improved, particularly outside expert centers. Prospective and multicenter trials are mandatory before introducing new software into clinical practice.

• 대한의생명과학회지
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• 제18권4호
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• pp.399-405
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• 2012

The brain is the body's most organized and controlled organ, and it governs various psychological and mental functions. A brain abnormality could greatly affect one's physical and mental abilities, and consequently one's social life. Brain disorders can be broadly categorized into three main afflictions: stroke, brain tumor, and dementia. Among these, stroke is a common disease that occurs owing to a disorder in blood flow, and it is accompanied by a sudden loss of consciousness and motor paralysis. The main types of strokes are infarction and hemorrhage. The exact diagnosis and early treatment of an infarction are very important for the patient's prognosis and for the determination of the treatment direction. In this study, texture features were analyzed in order to develop a prototype auto-diagnostic system for infarction using computer auto-diagnostic software. The analysis results indicate that of the six parameters measured, the average brightness, average contrast, flatness, and uniformity show a high cognition rate whereas the degree of skewness and entropy show a low cognition rate. On the basis of these results, it was suggested that a digital CT image obtained using the computer auto-diagnostic software can be used to provide valuable information for general CT image auto-detection and diagnosis for pre-reading. This system is highly advantageous because it can achieve early diagnosis of the disease and it can be used as supplementary data in image reading. Further, it is expected to enable accurate medical image detection and reduced diagnostic time in final-reading.

• 한국멀티미디어학회논문지
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• 제16권5호
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• pp.558-565
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• 2013

X-ray를 이용한 여성의 유방암 검사인 유방조영술은 유방암의 초기 단계에서의 진단을 위한 효과적인 방법이다. 컴퓨터 지원 검출(CAD) 시스템은 유방조영술을 통한 진단 시 의사가 놓치기 쉬운 유방암의 징후인 종괴의 검출을 도와 유방암 진단율을 높이는 수단이다. 종괴는 다양한 모양을 지니며 경계가 뚜렷하지 않기 때문에 검출이 어렵고 결과적으로 비-종괴 영역을 포함한 많은 수의 종괴 후보영역이 CAD 시스템에서 검출된다. 따라서 CAD 시스템 설계 시 검출된 많은 수의 종괴 후보영역으로부터 실제 악성 종괴 영역을 분류할 수 있도록 우수한 성능의 분류기가 요구된다. 본 논문에서는 피셔 분별 사전학습을 통해 개선된 Sparse 표현(SR) 기반 분류방법을 제안한다. 개선된 SR 기반 분류기가 기존의 CAD 시스템에서 주로 사용되어온 Support Vector Machine (SVM) 분류기 보다 우수함을 비교실험을 통해 확인했다.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1997년도 춘계학술대회 논문집
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• pp.190-195
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• 1997

Abstract: In this paper,an autoematic gear inspection system has been been developed using the computer aided vision system. Image processing and data analysis algorithms for gear inspection have been investigated and shown to perform quickly with high accuracy. As a result,dimensions of a gear can be measured upto few micrometer size in real time. In addition, the system can be applied to a practical manufacturing process even under nosiy conditions.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1996년도 추계학술대회 논문집
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• pp.485-489
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• 1996

In this paper, an automatic gear inspection system has been developed using the computer aided vision system. Image processing and data analysis algorithms for gear inspection have been investigated and were shown to perform quickly with high accuracy. As a result, dimensions of a gear can be measured upto few micrometer size in real time. In addition, the system can be applied to a practical manufacturing process even under noisy conditions.

• Korean Journal of Radiology
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• 제23권9호
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• pp.847-852
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• 2022

• 말소리와 음성과학
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• 제1권2호
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• pp.15-22
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• 2009

Prominence of speech, which is often called 'accent,' affects the fluency of speaking American English greatly. In this paper, we present an accurate prominence detection method that can be utilized in computer-aided language learning (CALL) systems. We employed pitch movement, overall syllable energy, 300-2200 Hz band energy, syllable duration, and spectral and temporal correlation as features to model the prominence of speech. After the features for vowel syllables of speech were extracted, prominent syllables were classified by SVM (Support Vector Machine). To further improve accuracy, the differences in characteristics of neighboring syllables were added as additional features. We also applied a speech recognizer to extract more precise syllable boundaries. The performance of our prominence detector was measured based on the Intonational Variation in English (IViE) speech corpus. We obtained 84.9% accuracy which is about 10% higher than previous research.