• Nuclear Engineering and Technology
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• 제55권10호
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• pp.3844-3853
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• 2023

Although radiation and chemotherapy methods for cancer therapy have advanced significantly, surgical resection is still recommended for most cancers. Therefore, intraoperative imaging studies have emerged as a surgical tool for identifying tumor margins. Intraoperative imaging has been examined using conventional imaging devices, such as optical near-infrared probes, gamma probes, and ultrasound devices. However, each modality has its limitations, such as depth penetration and spatial resolution. To overcome these limitations, hybrid imaging modalities and tracer studies are being developed. In a previous study, a multi-modal laparoscope with silicon photo-multiplier (SiPM)-based gamma detection acquired a 1 s interval gamma image. However, improvements in the near-infrared fluorophore (NIRF) signal intensity and gamma image central defects are needed to further evaluate the usefulness of multi-modal systems. In this study, an attempt was made to change the NIRF image acquisition method and the SiPM-based gamma detector to improve the source detection ability and reduce the image acquisition time. The performance of the multi-modal system using a complementary metal oxide semiconductor and modified SiPM gamma detector was evaluated in a phantom test. In future studies, a multi-modal system will be further optimized for pilot preclinical studies.

• 한국측량학회:학술대회논문집
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• 한국측량학회 2004년도 춘계학술발표회논문집
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• pp.177-182
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• 2004

This study presents the application of aerial photographs and KOMPSAT-1 Electro-Optical Camera(EOC) imagery in detecting the change of an urban area that has been rapidly growing. For the study, we used multi-time images which were acquired by two different sensors. For all of the images, the coordinate reference system and scale were first made identical through the 1st and 2nd geometric corrections and then image resampling were carried out to spatial resolution of 7m to detect changes under the same conditions. The Image Differencing was employed as a change detection technique. It was confirmed to be able to detect the changes of terrestrial surface like building, structure and road features from aerial photos and KOMPSAT EOC images with single band. The changes could be detected to some extent with the images acquired from different kinds of sensors as well as the same kinds of sensors.

• 제어로봇시스템학회논문지
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• 제19권1호
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• pp.72-79
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• 2013

Different scanning methods can be used to detect targets of interest in an image. In this paper, four scanning methods, generalized raster scanning, radial scanning, corner scanning, and random scanning, are considered for the evaluation of their scanning performances. The scanning performance is defined here as the ratio of the average scanning area required to detect a single target to the whole image area. Analytic expressions for the performance of each scanning method are derived. Computational results are given to illustrate the usage and validity of the expressions for the performance comparison.

• 한국섬유공학회:학술대회논문집
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• 한국섬유공학회 2003년도 The Korea-Japan Joint Symposium
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• pp.100-100
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• 2003

An image processing algorithm has been developed in order to analyze the nanofiber web images obtained from a high magnification microscope. It has been known that precise pore detection on thick webs is extremely difficult mainly due to lack of light uniformity, difficulty of fine focusing and translucency of nanofiber web. The pore detection algorithm developed has been found to show excellent performance in characterizing the porous structure, thus being a promising tool for on-line quality control system under mass production. Since the images obtained from an optical microscope represent only web surface, a scale factor has been introduced to estimate the web structure as a whole. Resulting web structures have been compared to those by mercury porosimetry, especially in pore size distribution. It has been shown that those two structures have a strong correlation, indicating that scaling of a single layer web structure can be an effective way of estimating the structure of thick fiber webs.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1994년도 Proceedings of the Korea Automatic Control Conference, 9th (KACC) ; Taejeon, Korea; 17-20 Oct. 1994
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• pp.601-604
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• 1994

This paper describes an effective method to estimate a position of an automous vehicle equipped with a single CCD-camera along indoor passageways. Using the sequential image data from the self-movement of the vehicle, the position is estimated by integrating the approximated motion parameters. The detection of the yaw angle that is one of the motion parameter is difficult in general, e.g. slip or error for noise, therefore the different detection is presented, which is, without shaft encoders, based on a projection function for 2D-image data and a cross-correlation function so as to be robust for noise. The approximated geometric function to estimate the position is used to reduce the computational effort. To verify the effectiveness of the method, the analysis and the computational results are shown through the simulations. Furthermore, the experimental results by using the test vehicle for the real indoor passageway are shown.

• 융합신호처리학회논문지
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• 제19권2호
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• pp.35-41
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• 2018

The analysis of plankton species distribution in sea or fresh water is very important in preserving marine ecosystem health. Since manual analysis is infeasible, many automatic approaches were proposed. They usually use images from in situ towed underwater imaging sensor or specially designed, lab mounted microscopic imaging system. Normally they assume that only single plankton is present in an image so that, if there is a clumping among multiple plankton of same species (homogeneous clumping) or if there are multiple plankton of different species scattered in an image (heterogeneous interspersion), they have a difficulty in recognition. In this work, we propose a deep learning based method that can detect and recognize individual plankton in images with homogeneous clumping, heterogeneous interspersion, or combination of both.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.732-734
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• 2003

In the middle -resolution remote sensing, the Ground Sampled Distance(GSD) sensed and sampled by the detector is generally larger than the size of objects(or materials) of interest, in which case several objects are embedded in a single pixel and cannot be detected spatially. This study is intended to solve this problem of a hyperspectral data with high spectral resolution. We examined the detection algorithm, Linear Spectral Mixing Model, and also made a test on the Hyperion data. To find class Endmembers, we applied two methods, Spectral Library and Geometric Model, and compared them with each other.

• Journal of Information Processing Systems
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• 제15권1호
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• pp.47-54
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• 2019

This study is concerned with the mechanism and structure of an optical microscope and an automatic multi-focus algorithm for automatically selecting sharp images from multiple foci of a cell. To obtain precise cell images quickly, a z-axis actuator with a resolution of $0.1{\mu}m$ was designed to control an optical microscope Moreover, a lighting control system was constructed to select the color and brightness of light that best suit the object being viewed. Cell images are captured by the instrument and the sharpness of each image is determined using Gaussian and Laplacian filters. Next, cubic spline interpolation and peak detection algorithms are applied to automatically find the most vivid points among multiple images of a single object. A cancer cell imaging experiment using propidium iodide staining confirmed that a sharp multipoint image can be obtained using this microscope. The proposed system is expected to save time and effort required to extract suitable cell images and increase the convenience of cell analysis.

• 한국멀티미디어학회논문지
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• 제10권12호
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• pp.1566-1576
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• 2007

Camera pose information from 2D face image is very important for making virtual 3D face model synchronize with the real face. It is also very important for any other uses such as: human computer interface, 3D object estimation, automatic camera control etc. In this paper, we have presented a camera position determination algorithm from a single 2D face image using the relationship between mouth position information and face region boundary information. Our algorithm first corrects the color bias by a lighting compensation algorithm, then we nonlinearly transformed the image into $YC_bC_r$ color space and use the visible chrominance feature of face in this color space to detect human face region. And then for face candidate, use the nearly reversed relationship information between $C_b\;and\;C_r$ cluster of face feature to detect mouth position. And then we use the geometrical relationship between mouth position information and face region boundary information to determine rotation angles in both x-axis and y-axis of camera position and use the relationship between face region size information and Camera-Face distance information to determine the camera-face distance. Experimental results demonstrate the validity of our algorithm and the correct determination rate is accredited for applying it into practice.

• 국제학술발표논문집
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.1243-1244
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• 2022

In recent years, the growing interest in off-site construction has led to factories scaling up their manufacturing and production processes in the construction sector. Consequently, continuous large-scale site monitoring in low-variability environments, such as prefabricated components production plants (precast concrete production), has gained increasing importance. Although many studies on computer vision-based site monitoring have been conducted, challenges for deploying this technology for large-scale field applications still remain. One of the issues is collecting and transmitting vast amounts of video data. Continuous site monitoring systems are based on real-time video data collection and analysis, which requires excessive computational resources and network traffic. In addition, it is difficult to integrate various object information with different sizes and scales into a single scene. Various sizes and types of objects (e.g., workers, heavy equipment, and materials) exist in a plant production environment, and these objects should be detected simultaneously for effective site monitoring. However, with the existing object detection algorithms, it is difficult to simultaneously detect objects with significant differences in size because collecting and training massive amounts of object image data with various scales is necessary. This study thus developed a large-scale site monitoring system using edge computing and a small-object detection system to solve these problems. Edge computing is a distributed information technology architecture wherein the image or video data is processed near the originating source, not on a centralized server or cloud. By inferring information from the AI computing module equipped with CCTVs and communicating only the processed information with the server, it is possible to reduce excessive network traffic. Small-object detection is an innovative method to detect different-sized objects by cropping the raw image and setting the appropriate number of rows and columns for image splitting based on the target object size. This enables the detection of small objects from cropped and magnified images. The detected small objects can then be expressed in the original image. In the inference process, this study used the YOLO-v5 algorithm, known for its fast processing speed and widely used for real-time object detection. This method could effectively detect large and even small objects that were difficult to detect with the existing object detection algorithms. When the large-scale site monitoring system was tested, it performed well in detecting small objects, such as workers in a large-scale view of construction sites, which were inaccurately detected by the existing algorithms. Our next goal is to incorporate various safety monitoring and risk analysis algorithms into this system, such as collision risk estimation, based on the time-to-collision concept, enabling the optimization of safety routes by accumulating workers' paths and inferring the risky areas based on workers' trajectory patterns. Through such developments, this continuous large-scale site monitoring system can guide a construction plant's safety management system more effectively.