Earticle

본 연구는 환자 피폭선량의 감소를 위한 목적으로 일개 병원에서 시행되는 경부 ct 검사의 촬영조건에서 관 전압과 영상처리 알고리즘의 적절한 적용으로 기존 영상 화질과 유사한 촬영조건과 방사선량을 평가하였다. 2021년 6월부터 11월까지 내원한 여성 환자 150명을 대상으로 경부 CT 검사를 시행하였다. 촬영조건은 80kV, 100kV, 120kV 자동 노출 조절 방법으로 영상을 획득하였다. 쵤영 후 영상 재구성 알고리즘인 SAFIRE를 단계적 0, 1, 3, 5 로 적용하였고, 경부 주변 조직에 관심 영역을 설정하여 신호대잡음비(Signal to noise, SNR)와 대조도비(Contrast to noise ratio, CNR)로 영상을 정량적 분석하였다. 그룹 간 비교에서 SAFIRE의 강도가 증가할수록 노이즈가 감소 하였으며 신호대잡음비, 대조도비가 증가하는 양상을 보였다. 또한 120kV의 자동 노출 조절 방법과 SAFIRE 강도 3과 비교적 유사한 영상의 화질을 가지는 조건은 100kV의 자동 노출 조절 방법과 SAFIRE 강도 3으로 나타났다. 120kV 자동 노출 조절 방법에서 100kV 자동 노출 조절 방법으로 낮추었더니 방사선량이 13.73%로 감소하였다.

This study evaluated the imaging conditions and radiation dose similar to the existing image quality by appropriate application of tube voltage and image processing algorithm in the imaging conditions of a cervical ct test performed at a hospital for the purpose of reducing the patient's exposure dose. Cervical CT scans were performed on 150 female patients who visited the hospital from June to November 2021. Images were acquired by automatic exposure control method of 80kV, 100kV, and 120kV under the shooting conditions. SAFIRE, a post-image reconstruction algorithm, was applied in steps of 0, 1, 3, and 5, and the region of interest was set in the surrounding tissues of the neck to obtain images with Signal to Noise (SNR) and Contrast to Noise Ratio (CNR). was quantitatively analyzed. In comparison between groups, as the intensity of SAFIRE increased, the noise decreased, and the signal-to-noise ratio and contrast ratio increased. In addition, the conditions of the automatic exposure control method of 120kV and the image quality relatively similar to SAFIRE intensity 3 were the automatic exposure control method of 100kV and SAFIRE intensity 3. When it was lowered from the 120kV automatic exposure control method to the 100kV automatic exposure control method, the radiation dose was reduced to 13.73%.

본 논문은 영상처리 기반의 낙상 감지 알고리즘의 설계 및 구현에 관한 내용을 기술한다. 영상처리 기반의 낙상 감지 알고리즘은카메라로 획득한 입력 영상을 그레이 스케일 변환 후 배경차분과 이진화를 통해 객체를 분리하고, 라벨링을 통해 인체를 인식한다. 인식된 인체는 출력 영상으로 확인이 가능하며 낙상을 감지하게 되면 알람이 발생한다. 컴퓨터 시뮬레이션을 통하여 제안한 알고리즘을 실험한 결과 90%의 검출율을 보여주었다. DSP 영상처리 보드에 구현한 시제품 시험을 통하여 기능을 검증함으로서 실용화가능성을 확인하였다.

This paper describes the design and implementation of fall-down detection algorithm based on image processing. The fall-down detection algorithm separates objects by using background subtraction and binarization after grayscale conversion of the input image acquired by the camera, and recognizes the human body by using labeling operation. The recognized human body can be monitored on the display image, and an alarm is generated when fall-down is detected. By using computer simulation, the proposed algorithm has shown a detection rate of 90%. We verify the feasibility of the proposed system by verifying the function by using the prototype test implemented on the DSP image processing board.

본 논문은 시각 장애인들을 위해 영상처리 기반의 숫자-자동 점자 변환기의 설계 및 구현에 관한 내용을 기술한다. 영상처리 기반의숫자-점자 변환 알고리즘은 카메라로 획득한 입력 영상을 이진 영상화 한 다음, 문자 영역을 팽창과 라벨링 연산을 수행하고 저장되어 있는 문자 패턴 영상과 상호 상관도를 계산하여 해당되는 점자로 변환한다. 컴퓨터 시뮬레이션을 통하여 제안한 알고리즘을 모의실험한 결과, A4 용지에 인쇄된 숫자(0-9)에 대하여 91.8% 변환 성공률을 보여 주었고, DSP 영상처리 보드에 구현한 시제품 시험을통하여 90% 변환 성능을 확인함으로서 구현된 숫자-자동 점자 변환기의 실용화 가능성을 확인하였다.

This paper describes the implementation of automatic number-braille converter based on image processing for the blind people. The algorithm is consists of four main steps. First step is binary image conversion of the input image obtained by the camera. the second step is segmentation operation by means of dilation and labelling of the character. Next step is calculation of cross-correlation between segmented text image and pre-defined text-pattern image. The final step is generation of brail output which is relevant to input image. The computer simulation result was showing 91.8% correct conversion rate for arabian numbers which is printed in A4-sheet and practical possibility was also confirmed by using implemented automatic number-braille converter based on DSP image processing board.

본 논문에서는 위성영상처리 소프트웨어 및 시스템의 재사용성을 높이고, 개발기간 및 유지 관리 비용을 줄일 수 있는 알고리즘컴포넌트화를 통한 위성영상처리 프레임워크 및 시스템에 대한 구조를 제안한다. 기존 위성영상처리 소프트웨어 및 시스템은 특정데이터 및 기능에 국한되어 제한된 구조를 갖고 있다. 또한, 각각의 시스템들은 동일 및 유사한 영상처리 알고리즘이 사용되지만해당 알고리즘을 중복적으로 개발하는 문제점이 있다. 이와 같은 문제점을 해결하기 위해 위성영상처리 소프트웨어 프레임워크의요구사항을 분석하였다. 요구사항을 반영한 프레임워크 및 시스템 구조를 설계하였으며, 위성영상처리 프레임워크의 운영흐름도 함께 도출하였다.

This paper suggest meteorological satellite processing software’s structure that reduces time and efforts of modification/upgrade. This structure’s key feature is “algorithm component” that works within framework and eventually to a completeMeteorological satellite processing system. Most of existing Meteorological satellite system is designed around specificfunction and data sets which limits range of modification and upgrade. In addition, re-use of current algorithms becomedifficult although re-use of similar algorithm is the case in many occasions. This inefficiency can be resolved by designinga new framework as a result of detail analysis of collected requirements. A new framework and system architecture has beendesigned. In addition, operational flow of Satellite image processing framework has been described.

In this paper, we proposed and tested an indoor obstacle recognition and avoidance algorithm using vision and ultrasonic sensors for effective operation of drone with low-power. In this paper, the indoor flight of a drone is mainly composed of two algorithms. First, for the indoor flight of the drone, the vanishing point and the center point of the image were extracted through Hough transform of the input image of the vision sensor. The drone moves along the extracted vanishing point. Second, we set an area of interest so that the drone can avoid obstacles. The area of interest is a space where the drone can fly after recognizing an obstacle at a distance from the ultrasonic sensor. When an obstacle is recognized in the drone's area of ​​interest, the drone performs an obstacle avoidance action. To verify the algorithm proposed in this paper, a simple obstacle was installed in an indoor environment and the drone was flown. From the experimental results, the proposed algorithm confirmed the indoor flight and obstacle avoidance behavior of the drone according to the vanishing point.

With the continuous development of computer science and technology, image processing and analysis gradually form the scientific system. Although history of image processing is not long, it attracts many researchers study on it. Digital media image widely exists in many fields, such as education, video, advertisement, and so on. Process digital media image is an important part of image processing. When analyze the digital media image, we want to extract the image part we care from the original image and then method for image segmentation is quite important. That is to say that the image segmentation will divide the image into a number of regions with specific and unique nature. How to keep the original characteristics of the digital media image is quite important in the image segmentation. In this paper, we propose a new algorithm for digital media image segmentation, and it is also can be used in the image processing. The algorithm is based on asynchronous particle swarm optimization algorithm to obtain the adaptive threshold; take the inertia factor into the algorithm, the optimal threshold has been acquired for the image segmentation. Compared with other particle swarm optimization algorithm, the algorithm has the advantages of stable, easy to converge to the optimal solution, and high segmentation speed.

Techniques of generating a single panoramic image by using multiple images are being widely studied in a number of areas, such as computer vision and computer graphics. Generating a panoramic image is a good way of overcoming the limitations of the images obtained from one single camera (e.g., those of picture angles, resolutions, information amounts, etc.) and may be applied in a variety of fields such as virtual reality and robot vision that require wide-angle images. A panoramic image has a great significance in that it can provide a greater sensation of immersion compared to a single image. Currently, there are a variety of techniques of producing panoramic images, but most of them commonly use a method of detecting feature points and matching points in each of the panoramic images they generate. In addition, they use the method of converting images after obtaining homography matrix using the RANSAC (Random Sample Consensus) algorithm that uses matching points. The SURF (Speeded Up Robust Features) algorithm used in this study utilizes the black-and-white and local space information of images when detecting their feature points and is widely used because it provides an outstanding performance in detecting the viewpoints and the changes of the image sizes and is faster than SIFT (Scale Invariant Features Transform) algorithm. However, the SURF algorithm also has its weak point of detecting wrong matching points, which may slow down the performance speed of the RANSAC algorithm and thus increases CPU usage occupation rates. The errors in detecting matching points serve as essential elements of lowering the accuracy and resolutions of panoramic images. In order to minimize these errors, this paper went through an intermediate filtering process of removing wrong matching points using the RGB values of 3×3 region around their coordinates and then presented analysis and evaluation results related to improvements in panoramic image construction & processing and CPU usage occupation rates and the decreasing rates and accuracy of the extracted matching points.

3차원 입체 방송을 가능하게 하기 위해서는 실세계에 존재하는 한 사물에 대한 깊이 정보를 획득하여야 한다. 따라서 본 논문에서는 네트워크 알고리즘인 신뢰확산(belief propagation) 알고리즘을 다해상도 영역에서 적용하여 3차원 정보의 근간이 되는 변이(disparity) 영상이나 깊이(depth)영상을 정확하면서도 빠르게 생성하는 것을 목적으로 한다. 신뢰확산 알고리즘은 기본적으로 여러 번의 반복을 통하여 변이정보를 보다 정확하게 갱신하게 되어 많은 연산량과 넓은 탐색영역으로 인하여 성능의 수렴까지 오랜 시간이 걸린다. 다해상도 변환은 공간영역과 주파수영역 모두에서 우수한 해상도를 갖기 때문에 이를 이용하여 스테레오 정합의 연산 속도를 증가시키고 성능을 향상시키는 것을 보여주었다.

3D image must have depth image for depth information in order for 3D realistic media broadcasting. We used generally belief propagation algorithm to solve probability model. Belief propagation algorithm is operated by message passing between nodes corresponding to each pixel. The high resolution image will be able to precisely represent but that required much computational complexity for 3D representation. We proposed fast stereo matching algorithm using belief propagation with multi-resolution based wavelet or lifting. This method can be shown efficiently computational time at much iterations for accurate disparity map.

In aluminum GMAW processes, moisture in the atmosphere is dissociated into hydrogen by the arc plasma, and the resulting hydrogen readily dissolves into the weld pool due to the high hydrogen solubility of aluminum. Although most of the dissolved hydrogen escapes to the atmosphere during solidification, a portion that fails to diffuse out becomes trapped within the weld and forms porosity. These pores are critical defects that can significantly degrade the mechanical performance of welded joints; therefore, cross-sectional analysis and various non-destructive testing (NDT) techniques are commonly used for inspection. However, cross-sectional analysis provides only localized information and is insufficient for evaluating defect distribution along the entire weld. Moreover, X-ray radiography often contains noise and irrelevant artifacts, making accurate quantitative assessment challenging. In this study, GMAW was performed on Al6061-T6 plates under conditions conducive to porosity formation, and X-ray radiographic images were acquired to capture defect information across the full weld region. MATLAB-based image processing techniques combined with a circular Hough transform algorithm were applied to quantitatively detect pore location, count, and diameter.

This paper proposes an image processing algorithm for detecting obstacles on road-lane using DLI(disparity of lane-related information) that is generated by stereo images acquired from dual cameras mounted on a moving vehicle. The DLI is a disparity that is acquired using single lane information from road lane detection. For the purpose to reduce processing time, we use small blocks obtained by edge-histogram based blocking logic. This algorithm detects moving objects such as preceding vehicles and obstacles. The proposed algorithm has been implemented in a personal computer with the road image data of a typical highway. We successfully performed experiments under a wide variety of road conditions without changing parameter values or adding human intervention. Experimental results also showed that the proposed DLI is quite successful.

TRISO (Tri-Isotropic)-coated fuel particle is widely applied due to its higher stability at high temperature and its efficient retention capability for fission products in the HTGR (high temperature gas-cooled reactor), one of the highly efficient Generation IV reactors. The typical ball-type TRISO-coated fuel particle with a diameter of about 1 mm is composed of a nuclear fuel particle as a kernel and of outer coating layers. The coating layers consist of a buffer PyC, inner PyC, SiC, and outer PyC layer. In this study, a digital image processing algorithm is proposed to automatically measure the thickness of the coating layers. An FBP (filtered backprojection) algorithm was applied to reconstruct the CT image using virtual X-ray radiographic images for a simulated TRISO-coated fuel particle. The automatic measurement algorithm was developed to measure the coating thickness for the reconstructed image with noises. The boundary lines were automatically detected, then the coating thickness was circularly by the algorithm. The simulation result showed that the measurement error rate was less than 1.4%.

In this paper, an algorithm to reconstruct two orthogonal images into a three-dimensional image is developed in order to measure the bubble size and volume in a two-phase boiling flow. The central-active contour model originally proposed by P. $Szczypi\'{n}ski$ and P. Strumillo is modified to reduce the dependence on the initial reference point and to increase the contour stability. The modified model is then applied to the algorithm to extract the object boundary. This improved central contour model could be applied to obscure objects using a variable threshold value. The extracted boundaries from each image are merged into a three-dimensional image through the developed algorithm. It is shown that the object reconstructed using the developed algorithm is very similar or identical to the real object. Various values such as volume and surface area are calculated for the reconstructed images and the developed algorithm is qualitatively verified using real images from rubber clay experiments and quantitatively verified by simulation using imaginary images. Finally, the developed algorithm is applied to measure the size and volume of vapor bubbles condensing in a subcooled boiling flow.

Adjustable reflective color gamut RGBW transflective liquid crystal display (ARC RGBW TRLCD) applied simple manufacture process and image processing algorithm to achieve high brightness and good color performance both in transmissive and reflective mode. With appropriate modification higher transmittance but no color distortion happens in transmissive mode. Moreover, base on superior reflectance total brightness and color gamut also can be modified under different ambience. It provides the flexibility not only for any environments but also for variant personal hobbies. It is the best technique used both at indoor and outdoor.

An algorithm has been developed to determine changes in surface topography on asperity level. The software stitches small but detailed images together to create one large image. If such an image is made before and after an experiment, their difference shows a direct 3D view of the changes in micro-geometry, rather than a change in surface parameters. The algorithm is described in detail and illustrated using artificial as well as real surfaces.

Purpose: The present study aimed at image processing methods for automatic cell pattern recognition and morphological analysis for tissue engineering applications. The primary aim was to ascertain the novel algorithm of adaptive brightness correction from microscopic images for use as a potential image analysis. Methods: General microscopic image of cells has a minor problem which the central area is brighter than edge-area because of the light source. This may affect serious problems to threshold process for cell-number counting or cell pattern recognition. In order to compensate the problem, we processed to find the central point of brightness and give less weight-value as the distance to centroid. Results: The results presented that microscopic images through the brightness correction were performed clearer than those without brightness compensation. And the classification of mixed cells was performed as well, which is expected to be completed with pattern recognition later. Beside each detection ratio of hBMSCs and HeLa cells was 95% and 92%, respectively. Conclusions: Using this novel algorithm of adaptive brightness correction could control the easier approach to cell pattern recognition and counting cell numbers.

Purpose: Recent mechatronics technology is the most appropriate high technology in agricultural applications to save repetitious labor. Method: Cow's body parameters were measured by several traditional measurers. Image processing technology was used to measure automatically their parameters to reduce labor and time. The parameters were measured form a small model cow which is easily measured, instead to a real cow. The image processing system designed and built for this project was composed of a PC, grabber card, and two cameras, which are located on the side and the top of the model cow. Tests of verification had measured 10 dairy cows. Result: Nine parameters of the model cow's body were measured, and the difference between the real data and the data by image processing was less than 16.7%. Based on the results of the research, the parameters of a real cow had measured of chest depth, withers height, Pelvic arch height, body length, slope body length, chest width, hip width, thurl width, and pin bone width were compared with image processing data. Conclusions: In the Demonstration test, Result had obtained similar data of cow model experiments, and the most of errors were shown less than 5% relatively good result.

The purpose of this research is to propose a system to detect a strip defect on a iron plate using an image processing, one way of finding defects on an iron plate. An existing way of image processing is using a light source which release a light energy in a certain frequency and a light absorbing display which responds to the light source. This research attempts to detect defects by using the image processing which handles an illumination, without depending on characteristics of light frequency. One of the advantages of this method is that it makes up for the weakness of the existing method which was too difficult for users to notice a defect. Also this method makes it possible to realize a real-time monitoring on a plate of iron.

This research describes a image-processing technique for the scratch detecting algorithm for ITO coated glass. We use the modified logical thresholding method for binarization of gray-scale glass image. This method is useful to the algorithm for detecting the scratch of ITO coated glass automatically without need of any prior information of manual fine-tuning of parameters.

전세계적으로 영상을 이용하는 분야는 급속도로 발전되고 확장되어 가고 있다. 이러한 시점에서 영상처리 기술의 중요성은 더욱더 강조되고 있다. 하지만 영상처리 기술은 다른 기술들에 비하여 굉장히 특화되어 같은 기술이라도 사용 환경과 분야에 따라 각기 다른 형태와 다른 사용 방식을 요구하고 있다. 이러한 환경은 영상처리 기술들의 재사용성을 떨어뜨리고 기술 개발 비용의 증가를 가져 왔다. 본 논문에서는 이러한 문제점들을 해결하고자 영상처리 기술을 활용하고 공유하기 위한 컴포넌트의 구조를 제안하고자 한다.