• 전기학회논문지P
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• 제53권1호
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• pp.1-6
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• 2004

In this paper, we present the new method for joint aspect inspecting system. We use the image processing and laser maker for light source. We can find the matrial joint status through processing the line pattern which is made by laser maker. To get the line pattern, in first, we did the preprocess of threshold. If the shape of line had over two segments, then the joint status is abnormal. We show our system efficency by experiment on tire facility.

• 대한전자공학회논문지SP
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• 제46권2호
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• pp.65-77
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• 2009

본 논문에서는 SSE (Streaming SIMD Extensions) 명령어를 이용한 고속 영상처리 알고리즘을 제안한다. SSE 명령어를 지원하는 CPU는 128비트 크기의 XMM 레지스터를 보유하고 있으며 이에 속한 데이터는 SIMD(Single Instruction Multiple Data) 방식으로 한 번에 병렬로 처리 될 수 있다. 영상처리에서 폭넓게 활용되는 평균 필터, 소벨 수평방향 외곽선 검출, 이진 침식 알고리즘을 SIMD 방식으로 효과적으로 처리 할 수 있는 알고리즘을 제시하였고, 수행 시간을 측정하였다. 보다 객관적인 수행 속도 평가를 위해 현재 많이 사용되고 있는 영상처리 라이브러리와의 수행 속도를 비교하였다. 비교에 사용된 라이브러리는 SISD(Single Instruction Single Data)방식으로 동작하는 OpenCV 1.0, SIMD 방식을 지원하는 고속 영상처리 라이브러리인 IPP 5.2와 MIL 8.0에서 각각 수행 시간을 측정하고 제안하는 알고리즘의 처리 속도와 비교하였다. 실험결과 제안하는 알고리즘은 SISD방식의 영상처리 라이브러리에 비해 평균 8배의 성능향상을 보였으며, SIMD 방식의 고속 영상처리 라이브러리와 비교 하였을 때 평균 1.4배의 성능향상을 보였다. 따라서 제안하는 알고리즘은 고가의 영상처리 라이브러리와 추가적인 하드웨어의 구입 없이도 고속으로 동작해야 하는 실제 영상 처리 어플리케이션에 효과적으로 적용될 수 있음을 보였다.

• Investigative Magnetic Resonance Imaging
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• 제23권2호
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• pp.81-99
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• 2019

Recently, deep learning methods have shown great potential in various tasks that involve handling large amounts of digital data. In the field of MR imaging research, deep learning methods are also rapidly being applied in a wide range of areas to complement or replace traditional model-based methods. Deep learning methods have shown remarkable improvements in several MR image processing areas such as image reconstruction, image quality improvement, parameter mapping, image contrast conversion, and image segmentation. With the current rapid development of deep learning technologies, the importance of the role of deep learning in MR imaging research appears to be growing. In this article, we introduce the basic concepts of deep learning and review recent studies on various MR image processing applications.

• International Journal of Advanced Culture Technology
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• 제11권1호
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• pp.283-289
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• 2023

AI is bringing about drastic changes not only in the aspect of technologies but also in society and culture. Medical AI based on deep learning have developed rapidly. Especially, the field of medical image analysis has been proven that AI can identify the characteristics of medical images more accurately and quickly than clinicians. Evaluating the latest results of the AI-based medical image processing is important for the implication for the development direction of medical AI. In this paper, we analyze and evaluate the latest trends in AI-based medical image analysis, which is showing great achievements in the field of medical AI in the healthcare industry. We analyze deep learning models for medical image analysis and AI-based medical image segmentation for quantitative analysis. Also, we evaluate the future development direction in terms of marketability as well as the size and characteristics of the medical AI market and the restrictions to market growth. For evaluating the latest trend in the deep learning-based medical image processing, we analyze the latest research results on the deep learning-based medical image processing and data of medical AI market. The analyzed trends provide the overall views and implication for the developing deep learning in the medical fields.

• International journal of advanced smart convergence
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• 제13권1호
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• pp.212-220
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• 2024

When a typhoon or natural disaster occurs, a significant number of orchard fruits fall. This has a great impact on the income of farmers. In this paper, we introduce an AI-based method to enhance low-quality raw images. Specifically, we focus on apple images, which are being used as AI training data. In this paper, we utilize both a basic program and an artificial intelligence model to conduct a general image process that determines the number of apples in an apple tree image. Our objective is to evaluate high and low performance based on the close proximity of the result to the actual number. The artificial intelligence models utilized in this study include the Convolutional Neural Network (CNN), VGG16, and RandomForest models, as well as a model utilizing traditional image processing techniques. The study found that 49 red apple fruits out of a total of 87 were identified in the apple tree image, resulting in a 62% hit rate after the general image process. The VGG16 model identified 61, corresponding to 88%, while the RandomForest model identified 32, corresponding to 83%. The CNN model identified 54, resulting in a 95% confirmation rate. Therefore, we aim to select an artificial intelligence model with outstanding performance and use a real-time object separation method employing artificial function and image processing techniques to identify orchard fruits. This application can notably enhance the income and convenience of orchard farmers.

• 전자공학회논문지B
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• 제31B권11호
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• pp.109-115
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• 1994

본 논문에서는 영상처리 방법을 이용하여 정자의 운동 특성을 분석하는 자동화된 방법을 개발하였다. 시스템의 구성은 별도의 전용 프로세서를 사용하지 않고 PC와 간단한 영상처리 보드로 이루어진다. 영상 처리 보드는 영상을 받아들이는 데 사용되며 PC는 영상을 처리한 다음 분석해서 정자의 운동 특성을 나타내 주는 특성 변수의 값을 계산한다. 분석 알고리듬으로서 중요한 것은 정자의 위치 검출 알고리듬과 정자의 운동 경로를 추적하는 Match matrix 방법이다. 분석한 결과를 수작업 방법 그리고 전용 프로세서를 이용하는 방법과 비교하여 신뢰할 만한 결과를 얻었다.

• 동력기계공학회지
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• 제15권1호
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• pp.18-23
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• 2011

Image processing has provided powerful techniques to extract from the acoustic signals the desired information on evaluation for leakage existence, leakage rate, and searching for leakage location, etc. The imagery NDE data available can add additional and significant dimension in nondestructive evaluation(NDE) information and thus for exploiting in applications. To extract such information the use of advanced image processing techniques is much needed. In recent years, there has been much increased use of acoustic signal image processing techniques in acoustic NDE. This approach will increase the efficiency of inspection procedures and reduce inspection time. In this paper we are concerned only with This paper is concerned mainly with the use of advanced image processing techniques in valve leakage detection and advanced image restoration and enhancement methods, which attempt to evaluate promptly by a visualization method the acoustic sources while detecting the valve leakage.

• Journal of Biosystems Engineering
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• 제33권3호
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• pp.196-203
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• 2008

Hybrid robust image processing algorithm to extract visual features of melon during the cultivation was developed based on a wireless tele-operative interface. Features of a melon such as size and shape including position were crucial to successful task automation and future development of cultivation data base. An algorithm was developed based on the concept of hybrid decision-making which shares a task between the computer and the operator utilizing man-computer interactive interface. A hybrid decision-making system was composed of three modules such as wireless image transmission, task specification and identification, and man-computer interface modules. Computing burden and the instability of the image processing results caused by the variation of illumination and the complexity of the environment caused by the irregular stem and shapes of leaves and shades were overcome using the proposed algorithm. With utilizing operator's teaching via LCD touch screen of the display monitor, the complexity and instability of the melon identification process has been avoided. Hough transform was modified for the image obtained from the locally specified window to extract the geometric shape and position of the melon. It took less than 200 milliseconds processing time.

• 정보처리학회논문지B
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• 제13B권2호
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• pp.83-88
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• 2006

일정한 형태의 물체를 분석하기 위해 사용되는 각-거리 그래프를 이용하여 임의의 물체의 경계선 내부 영역의 면적을 측정하는 디지털 영상처리 알고리듬을 제안한다. 물체의 경계선 내부의 한 점을 중심으로 1차 각-거리 그래프를 생성하고 이 그래프로부터 거리 값이 급격히 변화하는 위치를 추출하여 1차 그래프에서 접근하지 못한 영역을 인식하여 새 영역에서의 한 점을 중심으로 2차 각-거리 그래프를 생성한다. 물체의 형태가 복잡한 경우 차수가 증가하게 되며 이와 같이 계층적으로 구성된 각-거리 그래프 그룹에 대해 거리의 제곱을 각도 방향으로 적분하여 물체의 경계선 내부 영역의 면적을 측정한다.

• 동력기계공학회지
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• 제10권4호
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• pp.139-146
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• 2006

In this paper vector code correlation(VCC) method and an algorithm to promote the image processing performance in building an effective measurement system using cameras are described for automatically berthing and controlling the ship equipped with side thrusters. In order to realize automatic ship berthing, it is indispensable that the berthing assistant system on the ship should continuously trace a target in the berth to measure the distance to the target and the ship attitude, such that we can make the ship move to the specified location. The considered system is made up of 4 apparatuses compounded from a CCD camera, a camera direction controller, a popular PC with a built in image processing board and a signal conversion unit connected to parallel port of the PC. The object of this paper is to reduce the image processing time so that the berthing system is able to ensure the safety schedule against risks during approaching to the berth. It could be achieved by composing the vector code image to utilize the gradient of an approximated plane found with the brightness of pixels forming a certain region in an image and verifying the effectiveness on a commonly used PC. From experimental results, it is clear that the proposed method can be applied to the measurement system for automatic ship berthing and has the image processing time of fourfold as compared with the typical template matching method.