• Nuclear Medicine and Molecular Imaging
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• v.41 no.3
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• pp.234-240
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• 2007

Purpose: In this study we propose a block-iterative method for reconstructing Compton scattered data. This study shows that the well-known expectation maximization (EM) approach along with its accelerated version based on the ordered subsets principle can be applied to the problem of image reconstruction for Compton camera. This study also compares several methods of constructing subsets for optimal performance of our algorithms. Materials and Methods: Three reconstruction algorithms were implemented; simple backprojection (SBP), EM, and ordered subset EM (OSEM). For OSEM, the projection data were grouped into subsets in a predefined order. Three different schemes for choosing nonoverlapping subsets were considered; scatter angle-based subsets, detector position-based subsets, and both scatter angle- and detector position-based subsets. EM and OSEM with 16 subsets were performed with 64 and 4 iterations, respectively. The performance of each algorithm was evaluated in terms of computation time and normalized mean-squared error. Results: Both EM and OSEM clearly outperformed SBP in all aspects of accuracy. The OSEM with 16 subsets and 4 iterations, which is equivalent to the standard EM with 64 iterations, was approximately 14 times faster in computation time than the standard EM. In OSEM, all of the three schemes for choosing subsets yielded similar results in computation time as well as normalized mean-squared error. Conclusion: Our results show that the OSEM algorithm, which have proven useful in emission tomography, can also be applied to the problem of image reconstruction for Compton camera. With properly chosen subset construction methods and moderate numbers of subsets, our OSEM algorithm significantly improves the computational efficiency while keeping the original quality of the standard EM reconstruction. The OSEM algorithm with scatter angle- and detector position-based subsets is most available.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.1-23
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• 2018

From the 21st century, various high-quality services have come up with the growth of the internet or 'Information and Communication Technologies'. Especially, the scale of E-commerce industry in which Amazon and E-bay are standing out is exploding in a large way. As E-commerce grows, Customers could get what they want to buy easily while comparing various products because more products have been registered at online shopping malls. However, a problem has arisen with the growth of E-commerce. As too many products have been registered, it has become difficult for customers to search what they really need in the flood of products. When customers search for desired products with a generalized keyword, too many products have come out as a result. On the contrary, few products have been searched if customers type in details of products because concrete product-attributes have been registered rarely. In this situation, recognizing texts in images automatically with a machine can be a solution. Because bulk of product details are written in catalogs as image format, most of product information are not searched with text inputs in the current text-based searching system. It means if information in images can be converted to text format, customers can search products with product-details, which make them shop more conveniently. There are various existing OCR(Optical Character Recognition) programs which can recognize texts in images. But existing OCR programs are hard to be applied to catalog because they have problems in recognizing texts in certain circumstances, like texts are not big enough or fonts are not consistent. Therefore, this research suggests the way to recognize keywords in catalog with the Deep Learning algorithm which is state of the art in image-recognition area from 2010s. Single Shot Multibox Detector(SSD), which is a credited model for object-detection performance, can be used with structures re-designed to take into account the difference of text from object. But there is an issue that SSD model needs a lot of labeled-train data to be trained, because of the characteristic of deep learning algorithms, that it should be trained by supervised-learning. To collect data, we can try labelling location and classification information to texts in catalog manually. But if data are collected manually, many problems would come up. Some keywords would be missed because human can make mistakes while labelling train data. And it becomes too time-consuming to collect train data considering the scale of data needed or costly if a lot of workers are hired to shorten the time. Furthermore, if some specific keywords are needed to be trained, searching images that have the words would be difficult, as well. To solve the data issue, this research developed a program which create train data automatically. This program can make images which have various keywords and pictures like catalog and save location-information of keywords at the same time. With this program, not only data can be collected efficiently, but also the performance of SSD model becomes better. The SSD model recorded 81.99% of recognition rate with 20,000 data created by the program. Moreover, this research had an efficiency test of SSD model according to data differences to analyze what feature of data exert influence upon the performance of recognizing texts in images. As a result, it is figured out that the number of labeled keywords, the addition of overlapped keyword label, the existence of keywords that is not labeled, the spaces among keywords and the differences of background images are related to the performance of SSD model. This test can lead performance improvement of SSD model or other text-recognizing machine based on deep learning algorithm with high-quality data. SSD model which is re-designed to recognize texts in images and the program developed for creating train data are expected to contribute to improvement of searching system in E-commerce. Suppliers can put less time to register keywords for products and customers can search products with product-details which is written on the catalog.

• Korean Journal of Optics and Photonics
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• v.26 no.3
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• pp.162-167
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• 2015

In this paper, we developed a MTF (Modulation Transfer Function) measurement system using a knife-edge scanning method for infrared optics. It consists of an objective part to generate the target image, a collimator to make the beam parallel, and a detector to analyze the image. We used a tungsten filament as the light source and MCT (Mercury Cadmium Telluride) to detect the mid-infrared(wavelength $3-5{\mu}m$) image. We measured the MTF of a standard lens (f=5, material ZnSe) to test this instrument and compared the result to the theoretical value calculated using the ZEMAX commercial software. It was found that the difference was within ${\pm}0.035$ at the cut-off frequency (50 1/mm). Also, we calculated the A-type measurement uncertainty to check the reliability of the measurement. The result showed only 0.002 at 20 1/mm in spatial frequency, which means very little variation in the MTF measurement under the same conditions.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.3
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• pp.11-22
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• 2010

This paper is a study on implementation of intelligent image surveillance system using context information and supplements temporal-spatial constraint, the weak point in which it is hard to process it in real time. In this paper, we propose scene analysis algorithm which can be processed in real time in various environments at low resolution video(320*240) comprised of 30 frames per second. The proposed algorithm gets rid of background and meaningless frame among continuous frames. And, this paper uses wavelet transform and edge histogram to detect shot boundary. Next, representative key-frame in shot boundary is selected by key-frame selection parameter and edge histogram, mathematical morphology are used to detect only motion region. We define each four basic contexts in accordance with angles of feature points by applying vertical and horizontal ratio for the motion region of detected object. These are standing, laying, seating and walking. Finally, we carry out scene analysis by defining simple context model composed with general context and emergency context through estimating each context's connection status and configure a system in order to check real time processing possibility. The proposed system shows the performance of 92.5% in terms of recognition rate for a video of low resolution and processing speed is 0.74 second in average per frame, so that we can check real time processing is possible.

• Journal of KIISE:Databases
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• v.36 no.4
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• pp.263-279
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• 2009

Shape features in a content-based image retrieval (CBIR) system are divided into two classes: contour-based and region-based. Contour-based shape features are simple but they are not as efficient as region-based shape features. Most systems using the region-based shape feature have to extract the region firs t. The prior works on region-based systems still have shortcomings. They are complex to implement, particularly with respect to region extraction, and do not sufficiently use the spatial relationship between regions in the distance model In this paper, a region extraction method that is the combination of an edge-based method and a region growing method is proposed to accurately extract regions inside an object. Edges inside an object are accurately detected based on the Canny edge detector and the Hough transform. And the modified Integrated Region Matching (IRM) scheme which includes the adjacency relationship of regions is also proposed. It is used to compute the distance between images for the similarity search using shape features. The experimental results show the effectiveness of our region extraction method as well as the modified IRM. In comparison with other works, it is shown that the new region extraction method outperforms others.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.4
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• pp.94-101
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• 2009

In this paper, we propose design of hardware based on a high speed digital signal processor (DSP) and a field programmable gate array (FPGA) for real-time suppression of fixed pattern noise (FPN) using hardware neural networks (HNN) in cooled infrared focal plane array (IRFPA) imaging system FPN appears a limited operation by temperature in observable images which applies to non-uniformity correction for infrared detector. These have very important problems because it happen serious problem for other applications as well as degradation for image quality in our system Signal processing architecture for our system operates reference gain and offset values using three tables for low, normal, and high temperatures. Proposed method creates virtual tables to separate for overlapping region in three offset tables. We also choose an optimum tenn of temperature which controls weighted values of HNN using mean values of pixels in three regions. This operates gain and offset tables for low, normal, and high temperatures from mean values of pixels and it recursively don't have to do an offset compensation in operation of our system Based on experimental results, proposed method showed improved quality of image which suppressed FPN by change of temperature distribution from an observational image in real-time system.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.41 no.12
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• pp.59-65
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• 2004

A stable reference voltage generator is necessary to the infrared image signal readout circuit(ROIC) to improve noise characteristics of signal originated from infrared devices, that is, to gain good images. In this paper, bandgap circuit operating at cryogenic temperature of 77K for Infrared image ROIC(readout integrated circuit) was first made. It demonstrates practical use possibility through taking measurements and estimations. Bandgap circuit is a representative voltage reference circuit. Most of bandgap reference circuits which are presented so far operate at room temperature, and their characteristic are not suitable for infrared image ROIC operating at liquid nitrogen temperature, 77K. To design bandgap circuit operating at cryogenic temperature, suitable circuit is selected and the parameter characteristics of used devices as temperature change are seen by a theoretical study and fitted at liquid temperature with considering such characteristics. This circuit has been fabricated in the Hynix 0.6um standard CMOS process, and the output voltage measured shows that the stability is 1.042±0.0015V over the temperature range of 60K to 110K and is better than bandgap circuits operated at room temperature.

• Journal of KIISE:Software and Applications
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• v.30 no.9
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• pp.876-884
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• 2003

It is essential to identify both the pupil and iris boundaries for iris recognition. The circular edge detector proposed by Daugman is the most common and powerful method for the iris region extraction. The method is accurate but requires lots of computational time since it is based on the exhaustive search. Some heuristic methods have been proposed to reduce the computational time, but they are not as accurate as that of Daugman. In this paper, we propose a pupil and iris boundary finding algorithm which is faster than and as accurate as that of Daugman. The proposed algorithm searches the boundaries using the Daugman's circular edge detector, but reduces the search region using the problem domain knowledge. In order to find the pupil boundary, the search region is restricted in the maximum and minimum bounding circles in which the pupil resides. The bounding circles are obtained from the binarized pupil image. Two iris boundary points are obtained from the horizontal line passing through the center of the pupil region obtained above. These initial boundary points, together with the pupil point comprise two bounding circles. The iris boundary is searched in this bounding circles. Experiments show that the proposed algorithm is faster than that of Daugman and more accurate than the conventional heuristic methods.

• Journal of Radiation Protection and Research
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• v.39 no.3
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• pp.150-158
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• 2014

Radiation-related practitioners and radiation-treated patients at medical institutions are inevitably exposed to radiation for diagnosis and treatment. Although standards for maximum doses are recommended by the International Commission on Radiological Protection (ICPR) and the International Atomic Energy Agency (IAEA), more direct and available measurement and analytical methods are necessary for optimal exposure management for potential exposure subjects such as practitioners and patients. Thus, in this study we developed a system for real-time radiation monitoring at a distance that works with existing portable device. The monitoring system comprises three parts for detection, imaging, and transmission. For miniaturization of the detection part, a scintillation detector was designed based on a silicon photomultiplier (SiPM). The imaging part uses a wireless charge-coupled device (CCD) camera module along with the detection part to transmit a radiation image and measured data through the transmission part using a Bluetooth-enabled portable device. To evaluate the performance of the developed system, diagnostic X-ray generators and sources of $^{137}Cs$, $^{22}Na$, $^{60}Co$, $^{204}Tl$, and $^{90}Sr$ were used. We checked the results for reactivity to gamma, beta, and X-ray radiation and determined that the error range in the response linearity is less than 3% with regard to radiation strength and in the detection accuracy evaluation with regard to measured distance using MCNPX Code. We hope that the results of this study will contribute to cost savings for radiation detection system configuration and to individual exposure management.

• Applied Microscopy
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• v.34 no.1
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• pp.13-21
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• 2004

The high voltage electron microscope (HVEM) newly installed in KBSI is an advanced transmission electron microscope capable of atomic resolution (${\leq}1.2{\AA}$ point-to-point resolution) together with high titling function (${\pm}60^{\circ}$), which are suitable to do 3-dimensional atomic imaging of a specimen. In addition, the instrument can be controlled by remote operation system, named as 'FasTEM' for the HVEM, which is favorable to overcome some environmental obstacles resulting from the direct operation. The FasTEM remote operation system has been established between the headquarter of KBSI in Daejeon and the Seoul branch. The server system in the headquarter has been connected with a portable client console system in the Seoul branch using an advanced internet resource, 'KOREN' of 155 Mbps grade. Most of the HVEM functions essential to do remote operation are available on the portable client console. The experiment to acquire the high resolution image of [001] Au has been achieved by excellent transmission of control signals and communication with the HVEM. Real-time reaction like direct operation, such as controls of the illumination and projection parameters, acquisition and adjustment of each detector signal, and electrical steering of each motor-driven system has been realized in remote site. It is positively anticipated that the first remote operation of HVEM in conjunction with IT infraengineering plays a important role in constructing the network based e-Science Grid in Korea for national user s facilities.