철근의 한계상태변형률 평가를 위한 이미지 프로세싱의 적용

김성도,정치영,우태련,정진환 한국구조물진단유지관리공학회 2016 한국구조물진단유지관리공학회 논문집 Vol.20 No.3

In this study, measurements were conducted by image processing to do an in-depth evaluation of strain of rebar in a uniaxial tension test. The distribution of strain and the necking region were evaluated. The image processing is used to analyze the color information of a colored image, so that the parts consistent with desired targets can be distinguished from the other parts. After this process, the image was converted to a binary one. Centroids of each target region are obtained in the binary images. After repeating such process on the images from starting point to the finishing point of the test, elongation between targets is calculated based on the centroid of each target. The tensile test were conducted on grade 60 #7(D22) and #9(D29) rebars fabricated in accordance with ASTM A615 standards. Strain results from image processing were compared to the results from a conventional strain gauge, in order to see the validity of the image processing. With the image processing, the measuring was possible in not only the initial elastic region but also the necking region of more than 0.5(50%) strain. The image processing can remove the measuring limits as long as the targets can be video recorded. It also can measure strain at various spots because the targets can easily be attached and detached. Thus it is concluded that the image processing helps overcome limits in strain measuring and will be used in various ways. 본 연구에서는 철근의 인장시험에서 변형률에 대한 상세 측정을 위하여 이미지 프로세싱을 이용하고, 변형률 분포와 넥킹구간을 평가하였다. 이미지 프로세싱 방법으로는 기존의 회색조영상을 이용한 방법이 아닌 칼라영상의 색상정보를 분석하여, 원하는 타겟과 가장 일치하는 영역과 그 외의 영역으로 구분하여 이진영상으로 변환하는 방법을 사용하였다. 변환된 이진영상에서 개별 타겟들의 도심점을 산출한 후 각 도심점의 상대변위값을 변형률로 환산하였다. 인장실험은 ASTM A615 기준으로 제작된 grade 60 철근 중 D22와 D29에 대해서 시험을 수행하였다. 이미지 프로세싱을 이용하여 계측된 변형률 결과를 기존 변형률 게이지를 이용하여 계측한 결과와 비교하여, 본 연구에서 사용한 이미지 프로세싱 방법에 대해서 검증하였다. 이미지 프로세싱을 이용하여 초기 탄성구간의 변형률 뿐만 아니라 넥킹구간에서 발생한 0.5 (50%) 이상의 변형률도 계측이 가능한 것을 확인하였다. 본 연구결과 이미지 프로세싱을 통하여 기존 변형률 게이지의 계측한계를 극복가능하고, 다양한 지점에서 자유롭게 계측할 수 있음을 알 수 있었다.

Intracranial CT Angiography에서 3D 영상의 재현성 평가

윤동민(Dong Min Yoon),신상보(Sang Bo Shin),신철경(Sul Kyung Shin),남윤철(Yoon Chul Nam),김문찬(Moon Chan Kim) 대한CT영상기술학회 2012 대한CT영상기술학회지 Vol.14 No.1

목적 두개 내 동맥의 CT검사 후 3D processing을 하는 경우 사용되는 각 장비들을 비교하여 특징을 알아보고 3D processing시 유용한 방법에 대하여 고찰해 보고자 한다. 대상 및 방법 3D processing을 하기 위한 영상을 얻기 위해 Toshiba사의 64 channel MDCT(Aquilion TSX-101A, Toshiba, Japan)를 사용하여 Intracranial artery를 검사한 30명의 영상을 이용하였으며, 동일한 검사 부위의 조영제 주입 전 영상인 non-contrast image(이하 pre image)와 조영제 주입 후 영상인 post contrast image(이하 post image)를 이용하였다. Pre image와 post image를 3D Processing 장비 ADW(GE, version 4.4), Tera-Recon(Aquarius, version 3.7.0.13), Rapidia(Infinitt, version 2.8), Aquilion workstation(Toshiba, version TSX-101A), Intuition(Aquarius, version 4,4)에 각각 전송하고 자체 기능을 이용하여 bone을 제거하여 혈관부위만 남긴 영상을 만들어 ICA(internal carotid artery), BA(basilar artery), ACA(anterior cerebral artery), MCA(middle cerebral artery), PCA(posterior cerebral artery) 부위를 비교하였다. 결과 Rapidia와 Aquilion workstation에서는 CT number값을 이용하여 pre image와 post image를 적용하는 subtraction을 이용하고 있다. Rapidia와 Aquilion workstation에서 bone의 제거가 용이했고 혈관소실이 거의 없었다. Intuition은 display된 pre image와 post image의 pixel을 이용하여 A-B의 형식으로 계산하여 나타내고 이후 auto bone remove기능을 이용하여 혈관과 bone을 구분한다. Intuition은 bone의 제거가 특정부위에서 어려웠고 혈관소실도 있었다. ADW도 display된 pre image와 post image의 pixel을 이용하여 A-B의 형식으로 계산하여 나타내고 이후 auto bone remove기능을 이용하여 혈관과 bone을 구분한다. ADW는 bone의 제거는 용이했지만 특정부위의 혈관소실이 있었다. Tera-recon은 auto bone remove 기능을 이용하여 혈관에서 bone을 제거한다. Tera-recon에서는 bone의 제거가 특정부위에서 어려웠고 혈관소실도 있었다. 결론 CT검사 후 데이터를 이용하여 3D processing을 함에 있어 CT number값을 이용하여 pre image와 post image를 적용하는 subtraction의 기능을 이용하는 Rapidia와 Aquilion workstation의 사용이 보다 유용한 영상을 표현해내며 이는 원본 데이터의 왜곡을 최소화 하고 보다 실제에 가까운 3D 영상을 만든다. I. Purpose When using the 3D processing after the CT scan of intracranial arteries, study of best way to 3D processing and knowing the characteristics of each workstation. II. Material and methods For gain to the 3D image, 30 patients images that underwent CT Angiography, Intracranial Arteries by Toshiba 64 channel MDCT(Aquilion TSX-101A, Toshiba, Japan), images transferred to each 3D processing equipments. Pre and pat image was using the same location, and this images transfer to ADW(GE, version 4.4), Tera-Recon(Aquarius, version 3.7.0.13), Rapidia(Infinitt, version 2.8), Aquilion workstation(Toshiba, version TSX-101A), Intuition(Aquarius, version 4.4). Each workstation makes a 3D artery image without foreign object such as bone, vein, peripheral vessel, it using the self function at that time. Then, conμire the ICA(internal carotid artery), BA(basilar artery), ACA(anterior cerebral artery), MCA(middle cerebral artery), PCA (posterior cerebral artery). III. Result Rapidia and Aquilion workstation was using the CT number, then it takes a subtraction by pre and past image. Rapidia and Aquilion workstation was so useful equipments fee remove the bone and there was little or no loss of vessel. Intuition is using the display pixel of pre and past image, then appear to image such as A-B and compare vessel with bone by auto bone remove function. Intuition is hard to remove the bone and loss of vessel. ADW is also using the display pixel of pre and past image, then appear to image such as A-B and compare vessel with bone by auto bone remove function. ADW is useful equipments for remove the bone but it has loss of vessel. Tera-Recon takes a auto bone remove function and remove the bone from the vessel. Tera-Recon is hard hard to remove the bone and loss of vessel. IV. conclusion When using the 3D processing workstation for 3D image, Rapidia and Aquilion workstation subtraction function that apply to CT number of pre and post image is minimize distortion and so useful to mike a realistic 3D image.

철근의 한계상태변형률 평가를 위한 이미지 프로세싱의 적용

김상도,정치영,우태련,정진환 한국구조물진단유지관리공학회 2016 한국구조물진단유지관리공학회 논문집 Vol.20 No.3

본 연구에서는 철근의 인장시험에서 변형률에 대한 상세 측정을 위하여 이미지 프로세싱을 이용하고, 변형률 분포와 넥킹구간을 평가하였다. 이미지 프로세싱 방법으로는 기존의 회색조영상을 이용한 방법이 아닌 칼라영상의 색상정보를 분석하여, 원하는 타겟과 가장 일 치하는 영역과 그 외의 영역으로 구분하여 이진영상으로 변환하는 방법을 사용하였다. 변환된 이진영상에서 개별 타겟들의 도심점을 산출한 후 각 도심점의 상대변위값을 변형률로 환산하였다. 인장실험은 ASTM A615 기준으로 제작된 grade 60 철근 중 D22와 D29에 대해서 시험을 수행하였다. 이미지 프로세싱을 이용하여 계측된 변형률 결과를 기존 변형률 게이지를 이용하여 계측한 결과와 비교하여, 본 연구에서 사용한 이미지 프로세싱 방법에 대해서 검증하였다. 이미지 프로세싱을 이용하여 초기 탄성구간의 변형률 뿐만 아니라 넥킹구간에서 발생한 0.5 (50%) 이상의 변형률도 계측이 가능한 것을 확인하였다. 본 연구결과 이미지 프로세싱을 통하여 기존 변형률 게이지의 계측한계를 극복가능 하고, 다양한 지점에서 자유롭게 계측할 수 있음을 알 수 있었다. In this study, measurements were conducted by image processing to do an in-depth evaluation of strain of rebar in a uniaxial tension test. The distribution of strain and the necking region were evaluated. The image processing is used to analyze the color information of a colored image, so that the parts consistent with desired targets can be distinguished from the other parts. After this process, the image was converted to a binary one. Centroids of each target region are obtained in the binary images. After repeating such process on the images from starting point to the finishing point of the test, elongation between targets is calculated based on the centroid of each target. The tensile test were conducted on grade 60 #7(D22) and #9(D29) rebars fabricated in accordance with ASTM A615 standards. Strain results from image processing were compared to the results from a conventional strain gauge, in order to see the validity of the image processing. With the image processing, the measuring was possible in not only the initial elastic region but also the necking region of more than 0.5(50%) strain. The image processing can remove the measuring limits as long as the targets can be video recorded. It also can measure strain at various spots because the targets can easily be attached and detached. Thus it is concluded that the image processing helps overcome limits in strain measuring and will be used in various ways.

A Review on Modified Image Enhancement Applications

Mahendra PS Kuber,Manish Dixit 보안공학연구지원센터 2014 International Journal of Signal Processing, Image Vol.7 No.5

The aim of image enhancement is to or to provide ‘better’ input for other improve the interpretability or perception of information in images for human viewing automated image processing techniques. Various Histogram Equalization techniques like CHE, GHE, BBHE, DSIHE, RMSHE and Multi-HE techniques are used for processing the image input to enhance its output. This paper provides a review over the modification of the brightness preserving dynamic histogram equalization technique to improve its brightness preserving and contrast enhancement abilities while reducing its computational complexity. There are many modified technique related to brightness preserving dynamic Histogram Equalization that uses statistics of digital images for their representation and processing are discussed here. Representation and processing of images in the spatial domain enables the technique to handle the inexactness of gray level values in a better way, resulting in improved performance. This algorithm enhances image contrast as well as preserves the brightness very effectively. Some images are not available to good quality, so these algorithms are used for image enhancement to improve the quality of the image.

Application of Webcam for Inspection of Rice Grain Quality by Using Image Processing Technique

Witsarut Sriratana,Namo Narknam,Ruangsit Apichitanon,Narin Tammarugwattana 제어로봇시스템학회 2020 제어로봇시스템학회 국제학술대회 논문집 Vol.2020 No.10

This paper presents the image processing system for quality inspection of rice grains after sorting process by using an ordinary webcam to ensure that rice grains should meet the quality standards and impurity such as grits, dirt and stones should not be found [1]. Moreover, statistical information of rice grains from image processing could be taken into analysis to enhance the efficiency of the sorting process. Image data analysis of rice grains can be divided into 2 main arrangement conditions, non-overlapping and overlapping. In this study, the experiments to inspect rice grains were applied for 24 samples and 18 experimental designs including the complete and incomplete structures, the overlapping and non-overlapping arrangements, and the combination of rice grains from complete and incomplete structures in equal and unequal ratios. From experiments, it was found that the inspection of rice grain quality by using image processing technique and image data recorded by an ordinary webcam was reliable and efficient. The inspection errors under non-overlapping arrangement condition of complete and incomplete structures of rice grains were not observed. The inspection errors of rice grains combined from complete and incomplete structures in equal and unequal ratios were about 0.8% and 1.11%, respectively. However, under overlapping arrangement condition, it was observed that the inspection errors of image processing would be high by up to 53.82% on average.

Application of Improved SVM Algorithm in Color Image De-Noising

Dong Tao 보안공학연구지원센터 2016 International Journal of Signal Processing, Image Vol.9 No.8

It cannot avoid the noise interference in image processing, whether it is image generation, or image transmission, among them, the most typical noise is salt and pepper noise and Gaussian noise. The salt and pepper noise will cause the image showing the random distribution of noise points, thus greatly reduce the image quality. The Gaussian noise affects the input, collection and output of the image processing. Gaussian noise will make the image blurred. Therefore, the image de-noising plays a very important role in image processing. It has direct influence on image segmentation, feature extraction and image recognition. As is known to all, the support vector machine has the advantages of solving the problem of nonlinear, high dimension and local minimum points. In this article, we use this advantage to propose an image de-noising method which is based on it. The method uses support vector regression to construct the filter for image de-noising. The feature extraction and training samples are designed to suppress different types of noise. Firstly, we use the noise pixel as the center of the 5*5 window, and generate the input vector of SVM from row to column. Secondly, we set the output of the support vector filter as an image that is not contaminated by noise. At this point, we get the training samples of SVM filter. In addition, the parameter selection of support vector machine has a great influence on the result of image de-noising. Therefore, the particle swarm optimization algorithm is proposed in this article to optimize the parameters of SVM. Finally, we adding the simulated salt and pepper noise and Gaussian noise in the original Lena image, and using several methods to carry out the de-noising experiment. From the experimental results we can see that the de-noising effect of filtering algorithm of this paper is very good for the two kinds of noise. It can effectively remove the noise, and better maintain the details and the color of the image.

Dynamic Memory Access Control for Accelerating FPGA-based Image Processing

Kenta Nishiguchi,Toshiyuki Inoue,Rei Yamazaki,Kazunori Ogohara,Akira Tsuchiya,Keiji Kishine 대한전자공학회 2021 Journal of semiconductor technology and science Vol.21 No.1

In an FPGA-based image processing system, memory access plays an important role in increasing the speed of image processing. With the conventional method, the memory access interval for image processing was fixed, which limited the processing speed. In this work, we developed a novel memory access method that dynamically controls the intervals between memory access requests for image processing by monitoring the memory status. We implemented an image processing system with the proposed method and examined its characteristics. The maximum processing speed with the proposed method reached 30.7 fps, which was 1.65 times faster than that of the conventional method.

입술 움직임 영상 선호를 이용한 음성 구간 검출

김응규,Kim, Eung-Kyeu 한국융합신호처리학회 2010 융합신호처리학회 논문지 (JISPS) Vol.11 No.4

본 논문에서는 음성인식을 위한 음성구간 검출과정에서 유입될 수 있는 동적인 음향에너지 이외에 화자의 입술움직임 영상신호까지 확인함으로써 외부 음향잡음이 음성인식 대상으로 오인식되는 것을 방지하기 위한 한 가지 방법이 제시된다. 우선, 연속적인 영상이 PC용 영상카메라를 통하여 획득되고 그 입술움직임 여부가 식별된다. 다음으로, 입술움직임 영상신호 데이터는 공유메모리에 저장되어 음성인식 프로세서와 공유한다. 한편, 음성인식의 전처리 단계인 음성구간 검출과정에서 공유메모리에 저장되어진 데이터를 확인함으로써 화자의 발성에 의한 음향에너지인지의 여부가 입증된다. 최종적으로, 음성인식기와 영상처리기를 연동시켜 실험한 결과, 영상카메라에 대면해서 발성하면 음성인식 결과의 출력에 이르기까지 연동처리가 정상적으로 진행됨을 확인하였고, 영상카메라에 대면치 않고 발성하면 연동처리시스템이 그 음성인식 결과를 출력치 못함을 확인하였다. 또한, 오프라인하의 입술움직임 초기 특정값 및 템플릿 초기영상을 온라인하에서 추출된 입술움직임 초기특정값 및 템플릿 영상으로 대체함으로써 입술움직임 영상 추적의 변별력을 향상시켰다. 입술움직임 영상 추적과정을 시각적으로 확인하고 실시간으로 관련된 패러미터를 해석하기 위해 영상처리 테스트베드를 구축하였다, 음성과 영상처리 시스템의 연동결과 다양한 조명환경 하에서도 약 99.3%의 연동율을 나타냈다. In this paper, A method to prevent the external acoustic noise from being misrecognized as the speech recognition object is presented in the speech activity detection process for the speech recognition. Also this paper confirmed besides the acoustic energy to the lip movement image signals. First of all, the successive images are obtained through the image camera for personal computer and the lip movement whether or not is discriminated. The next, the lip movement image signal data is stored in the shared memory and shares with the speech recognition process. In the mean time, the acoustic energy whether or not by the utterance of a speaker is verified by confirming data stored in the shared memory in the speech activity detection process which is the preprocess phase of the speech recognition. Finally, as a experimental result of linking the speech recognition processor and the image processor, it is confirmed to be normal progression to the output of the speech recognition result if face to the image camera and speak. On the other hand, it is confirmed not to the output the result of the speech recognition if does not face to the image camera and speak. Also, the initial feature values under off-line are replaced by them. Similarly, the initial template image captured while off-line is replaced with a template image captured under on-line, so the discrimination of the lip movement image tracking is raised. An image processing test bed was implemented to confirm the lip movement image tracking process visually and to analyze the related parameters on a real-time basis. As a result of linking the speech and image processing system, the interworking rate shows 99.3% in the various illumination environments.

Off-Site Distortion and Color Compensation of Underwater Archaeological Images Photographed in the Very Turbid Yellow Sea

Young-Hwa Jung,Gyuho Kim,Woo Sik Yoo 한국문화재보존과학회 2022 보존과학회지 Vol.38 No.1

Underwater photographing and image recording are essential for pre-excavation survey and during excavation in underwater archaeology. Unlike photographing on land, all underwater images suffer various quality degradations such as shape distortions, color shift, blur, low contrast, high noise levels and so on. Outcome is very often heavily photographing equipment and photographer dependent. Excavation schedule, weather conditions, and water conditions can put burdens on divers. Usable images are very limited compared to the efforts. In underwater archaeological study in very turbid water such as in the Yellow Sea (between mainland China and the Korean peninsula), underwater photographing is very challenging. In this study, off-site image distortion and color compensation techniques using an image processing/analysis software is investigated as an alternative image quality enhancement method. As sample images, photographs taken during the excavation of 800-year-old Taean Mado Shipwrecks in the Yellow Sea in 2008-2010 were mainly used. Significant enhancement in distortion and color compensation of archived images were obtained by simple post image processing using image processing/analysis software (PicMan) customized for given view ports, lenses and cameras with and without optical axis offsets. Post image processing is found to be very effective in distortion and color compensation of both recent and archived images from various photographing equipment models and configurations. Merits and demerit of in-situ, distortion and color compensated photographing with sophisticated equipment and conventional photographing equipment, which requires post image processing, are compared.

Estimation of Crop Destruction from Ingestion by Animals in Grazing Field using Phase Correlation Analysis of Distorted Remote Image

( Yongjin Cho ),( Jong-woo Oh ),( Dong-hoon Lee ) 한국농업기계학회 2018 한국농업기계학회 학술발표논문집 Vol.23 No.1

This study was accomplished to estimate how fast animals destroyed grazing field as ingestion of crop using frequency domain analysis on drone image. The image were collected at the North Carolina A&T State University farm located in Greensboro, North Carolina every day, unless it rained or was too windy. Some limitations to take into account was that the drone did not collect images in the same aligned position every time. Further, the images produced from the drone were distorted by a fisheye effect. The experiment observed for two weeks based on how the animals destroyed the field from September 2016. Combining with the distinctive position of every picture taken, the images became ineffective pieces of information and evidence. MATLAB was utilized in efforts to adjust the picture for effective and accurate analysis to compare the fields after each day the animals were introduced to the grazing field. In step 1, image translation was identified between two timely adjacent images of rotation and distortion by next try. After cropping images from the center of each original image, the image translation was estimated by using imregcorr function, and the size of the cropped image was gradually enlarged. In step 2, similarities between two crop images calibrated by identified translation was analyzed as follows; a) Crop first image from center of first image, b) Crop second image from translated location in second image, c) Identify similarities between two cropped images using phase-correlation, d) Gradually enlarge the size of cropped image. After repeating steps 1 and 2 with the next adjacent pairs of images, adjacent images could be scaled to dry matter consumption ratio (assuming that the first image is 100% of forages, and the last image is 0% of forages). In conclusion, the most field was destroyed by animals between the 10th and 11th day after the animals were introduced into the grazing field. Air-bone image was collected by drone including systematic distortion error can be calibrated by consideration about frequency domain analysis such as phase correlation.