• 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.

• Journal of Radiation Industry
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• v.9 no.1
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• pp.1-7
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• 2015

The trend of x-ray image sensor has been evolved from an amorphous silicon sensor to a crystal silicon sensor. A crystal silicon X-ray sensor, meaning a X-ray CIS (CMOS image sensor), is consisted of three transistors (Trs), i.e., a Reset Transistor, a Source Follower and a Select Transistor, and a photodiode. They are highly sensitive to radiation exposure. As the frequency of exposure to radiation increases, the quality of the imaging device dramatically decreases. The most well known effects of a X-ray CIS due to the radiation damage are increments in the reset voltage and dark currents. In this study, a pixel array of a X-ray CIS was made of $20{\times}20pixels$ and this pixel array was exposed to a high radiation dose. The radiation source was Co-60 and the total radiation dose was increased from 1 to 9 kGy with a step of 1 kGy. We irradiated the small pixel array to get the increments data of the reset voltage and the dark currents. Also, we simulated the radiation effects of the pixel by MCNP (Monte Carlo N-Particle) simulation. From the comparison of actual data and simulation data, the most affected location could be determined and the cause of the increments of the reset voltage and dark current could be found.

• The Journal of Korean Society for Radiation Therapy
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• v.17 no.1
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• pp.19-31
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• 2005

Purpose : To supply the information of EPID system and to analyze the possibility of substitution EPID for film dosimetry. Materials & Methods : With amorphous silicon(aSi) type EPID and liquid filled lonization chamber(LC) type EPID, the reproducibility according to focus detector distance(FDD) change and gantry rotation was analyzed, and also the possible range of image acquisition was analyzed with Alderson Rando phantom. The resolution and the contrast of aSi type EPID image were analyzed through Las Vegas phantom and water phantom. DMLC image was analyzed with X-Omat V film and EPID to see wether it could be applied to the qualify assurance(QA) of IMRT. Results : The reproducibility of FDD position was within 1mm, but the reproducibility of gantry rotation was ${\pm}2,\;{\pm}3mm$ respectively. The resolution and the contrast of EPID image were affected by dose rate, image acquisition time, image acquisition method and frame number. According to the possible range of image acquisition of EPID, it is verified that the EPID is easier to use than film. There is no difference between X-Omat V film and EPID images for the QA of IMRT. Conclusion : Through various evaluation, we could obtain lots of useful information about the EPID. Because the EPID has digital data, also we found that the EPID is more useful than film dosimerty for the periodical Qualify Assurance of IMRT. Especially when it is difficult to do point dose measurement with diode or ionization chamber, the EPID could be very useful substitute. And we found that the diode and ionization chamber are difficult to evaluate the sliding window images of IMRT, but the EPID was more useful to do it.

• The Journal of the Korea Contents Association
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• v.16 no.7
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• pp.681-688
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• 2016

This study assess their quality of radiation on analysis of the spectrum of resolution suggesting IEC 61267 in radiation quality that RQA3, RQA5, RQA7, RQA9 and combination of clinical condition using several quality of radiation. In experiments edge method first, the spatial resolution assessment used image of the additional filter and SID is obtained the IEC 62220-1, spatial resolution and sharpness of the obtained image was evaluated in the MTF value 10%(0.1), MTF value 50%(0.5) using a Matlab program. Second, MCNPX simulation used spatial resolution analysis was radiation quality particle fluence and spectrum analysis in energy. As a result, make use of additional filter, image quality evaluation of SID that RQA3 radiation quality combination qualification is higher spatial resolution and sharpness make unused of additional filter and SID 100cm. RQA7 radiation quality combination qualification is higher that spatial resolution make unused of additional filter and SID 150cm. RQA9 radiation quality combination qualification is higher that spatial resolution and sharpness make used of additional filter and SID 180cm. spectrum analysis of radiation quality by reducing consequent errors occurring in the experiment that error due to the reproducibility of the X-ray tube, occur in an error of correction the detector suggest ideal conditions from spectrum analysis through MCNPX simulation. In conclusion, by suggesting spatial resolution and sharpness of result for various radiation quality, It provide basic data that radiation quality condition and quantitative assessment method for laboratory in clinical using detector evaluation.

• Progress in Medical Physics
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• v.7 no.1
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• pp.65-77
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• 1996

Using the 2-D and 3-D Hoffman brain phantom, 3-D Jaszczak phantom and Single Photon Emission Computed Tomography, the effects of data acquisition parameter, attenuation, noise, scatter and reconstruction algorithm on image quantitation as well as image quality were studied. For the data acquisition parameters, the images were acquired by changing the increment angle of rotation and the radius. The less increment angle of rotation resulted in superior image quality. Smaller radius from the center of rotation gave better image quality, since the resolution degraded as increasing the distance from detector to object increased. Using the flood data in Jaszczak phantom, the optimal attenuation coefficients were derived as 0.12cm$\^$-1/ for all collimators. Consequently, the all images were corrected for attenuation using the derived attenuation coefficients. It showed concave line profile without attenuation correction and flat line profile with attenuation correction in flood data obtained with jaszczak phantom. And the attenuation correction improved both image qulity and image quantitation. To study the effects of noise, the images were acquired for 1min, 2min, 5min, 10min, and 20min. The 20min image showed much better noise characteristics than 1min image indicating that increasing the counting time reduces the noise characteristics which follow the Poisson distribution. The images were also acquired using dual-energy windows, one for main photopeak and another one for scatter peak. The images were then compared with and without scatter correction. Scatter correction improved image quality so that the cold sphere and bar pattern in Jaszczak phantom were clearly visualized. Scatter correction was also applied to 3-D Hoffman brain phantom and resulted in better image quality. In conclusion, the SPECT images were significantly affected by the factors of data acquisition parameter, attenuation, noise, scatter, and reconstruction algorithm and these factors must be optimized or corrected to obtain the useful SPECT data in clinical applications.

• Journal of radiological science and technology
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• v.29 no.3
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• pp.125-132
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• 2006

The PET/CT scanner is an evolution in image technology. The two modalities are complementary with CT and PET images. The PET scan images are well known as low resolution anatomic landmak, but such problems may help with interpretation detailed anatomic framework such as that provided by CT scan. PET/CT offers some advantages-improved lesion localization and identification, more accurate tumor staging. etc. Conventional PET employs tranmission scan require around 4 min./bed position and 30 min. for whole body scan. But PET/CT scanner can reduced by 50% in whole body scan. Especially nowadays PET scanner LSO scintillator-based from BGO without septa and operate in 3-D acquisition mode with multidetectors CT. PET/CT scanner fusion problems solved through hardware rather than software. Such device provides with the capability to acquire accurately aligned anatomic and functional images from single scan. It is very important to effective detection from gamma ray source in PETdetector. And can be offer high quality diagnostic images. So we have study about detection processing of PET detector and high quality imaging process.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.47-60
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• 2012

Video data comes in the form of the unstructured and the complex structure. As the importance of efficient management and retrieval for video data increases, studies on the video parsing based on the visual features contained in the video contents are researched to reconstruct video data as the meaningful structure. The early studies on video parsing are focused on splitting video data into shots, but detecting the shot boundary defined with the physical boundary does not cosider the semantic association of video data. Recently, studies on structuralizing video shots having the semantic association to the video scene defined with the semantic boundary by utilizing clustering methods are actively progressed. Previous studies on detecting the video scene try to detect video scenes by utilizing clustering algorithms based on the similarity measure between video shots mainly depended on color features. However, the correct identification of a video shot or scene and the detection of the gradual transitions such as dissolve, fade and wipe are difficult because color features of video data contain a noise and are abruptly changed due to the intervention of an unexpected object. In this paper, to solve these problems, we propose the Scene Detector by using Color histogram, corner Edge and Object color histogram (SDCEO) that clusters similar shots organizing same event based on visual features including the color histogram, the corner edge and the object color histogram to detect video scenes. The SDCEO is worthy of notice in a sense that it uses the edge feature with the color feature, and as a result, it effectively detects the gradual transitions as well as the abrupt transitions. The SDCEO consists of the Shot Bound Identifier and the Video Scene Detector. The Shot Bound Identifier is comprised of the Color Histogram Analysis step and the Corner Edge Analysis step. In the Color Histogram Analysis step, SDCEO uses the color histogram feature to organizing shot boundaries. The color histogram, recording the percentage of each quantized color among all pixels in a frame, are chosen for their good performance, as also reported in other work of content-based image and video analysis. To organize shot boundaries, SDCEO joins associated sequential frames into shot boundaries by measuring the similarity of the color histogram between frames. In the Corner Edge Analysis step, SDCEO identifies the final shot boundaries by using the corner edge feature. SDCEO detect associated shot boundaries comparing the corner edge feature between the last frame of previous shot boundary and the first frame of next shot boundary. In the Key-frame Extraction step, SDCEO compares each frame with all frames and measures the similarity by using histogram euclidean distance, and then select the frame the most similar with all frames contained in same shot boundary as the key-frame. Video Scene Detector clusters associated shots organizing same event by utilizing the hierarchical agglomerative clustering method based on the visual features including the color histogram and the object color histogram. After detecting video scenes, SDCEO organizes final video scene by repetitive clustering until the simiarity distance between shot boundaries less than the threshold h. In this paper, we construct the prototype of SDCEO and experiments are carried out with the baseline data that are manually constructed, and the experimental results that the precision of shot boundary detection is 93.3% and the precision of video scene detection is 83.3% are satisfactory.

• The Korean Journal of Nuclear Medicine Technology
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• v.16 no.1
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• pp.12-16
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• 2012

Purpose : In conventional PET image reconstruction, iterative reconstruction methods such as OSEM (Ordered Subsets Expectation Maximization) have now generally replaced traditional analytic methods such as filtered back-projection. This includes improvements in components of the system model geometry, fully 3D scatter and low noise randoms estimates. SharpIR algorithm is to improve PET image contrast to noise by incorporating information about the PET detector response into the 3D iterative reconstruction algorithm. The aim of this study is evaluation of SharpIR reconstruction method in PET/CT. Materials and Methods: For the measurement of detector response for the spatial resolution, a capillary tube was filled with FDG and scanned at varying distances from the iso-center (5, 10, 15, 20 cm). To measure image quality for contrast recovery, the NEMA IEC body phantom (Data Spectrum Corporation, Hillsborough, NC) with diameters of 1, 13, 17 and 22 for simulating hot and 28 and 37 mm for simulating cold lesions. A solution of 5.4 kBq/mL of $^{18}F$-FDG in water was used as a radioactive background obtaining a lesion of background ratio of 4.0. Images were reconstructed with VUE point HD and VUE point HD using SharpIR reconstruction algorithm. For the clinical evaluation, a whole body FDG scan acquired and to demonstrate contrast recovery, ROIs were drawn on a metabolic hot spot and also on a uniform region of the liver. Images were reconstructed with function of varying iteration number (1~10). Results: The result of increases axial distance from iso-center, full width at half maximum (FWHM) is also increasing in VUE point HD reconstruction image. Even showed an increasing distances constant FWHM. VUE point HD with SharpIR than VUE point HD showed improves contrast recovery in phantom and clinical study. Conclusion: By incorporating more information about the detector system response, the SharpIR algorithm improves the accuracy of underlying model used in VUE point HD. SharpIR algorithm improve spatial resolution for a line source in air, and improves contrast recovery at equivalent noise levels in phantoms and clinical studies. Therefore, SharpIR algorithm can be applied as through a longitudinal study will be useful in clinical.

• Smart Structures and Systems
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• v.24 no.6
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• pp.745-757
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• 2019

Road surface deteriorations such as potholes have caused motorists heavy monetary damages every year. However, effective road condition monitoring has been a continuing challenge to road owners. Depth cameras have a small field of view and can be easily affected by vehicle bouncing. Traditional image processing methods based on algorithms such as segmentation cannot adapt to varying environmental and camera scenarios. In recent years, novel object detection methods based on deep learning algorithms have produced good results in detecting typical objects, such as faces, vehicles, structures and more, even in scenarios with changing object distances, camera angles, lighting conditions, etc. Therefore, in this study, a Deep Learning Pothole Detector (DLPD) based on the deep region-based convolutional neural network is proposed for autonomous detection of potholes from images. About 900 images with potholes and road surface conditions are collected and divided into training and testing data. Parameters of the network in the DLPD are calibrated based on sensitivity tests. Then, the calibrated DLPD is trained by the training data and applied to the 215 testing images to evaluate its performance. It is demonstrated that potholes can be automatically detected with high average precision over 93%. Potholes can be differentiated from manholes by training and applying a manhole-pothole classifier which is constructed using the convolutional neural network layers in DLPD. Repeated detection of the same potholes can be prevented through feature matching of the newly detected pothole with previously detected potholes within a small region.

• Journal of Astronomy and Space Sciences
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• v.40 no.4
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• pp.237-245
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• 2023

Laser communication has been considered as a novel method for earth observation satellites with generation of high data volume. It offers faster data transmission speeds compared to conventional radio frequency (RF) communication due to the short wavelength and narrow beam divergence. However, laser beams are refracted due to atmospheric turbulence between the ground and the satellite. Refracted laser beams, upon reaching the receiver, result in angle-of-arrival (AoA) fluctuation, inducing image dancing and wavefront distortion. These phenomena hinder signal acquisition and lead to signal loss in the course of laser communication. So, precise alignment between the transmitter and receiver is essential to guarantee effective and reliable laser communication, which is achieved by pointing, acquisition, and tracking (PAT) system. In this study, we simulate the effectiveness of tip/tilt compensation for more efficient laser communication in the satellite-ground downlink. By compensating for low-order terms using tip/tilt mirror, we verify the alleviation of AoA fluctuations under both weak and strong atmospheric turbulence conditions. And the performance of tip/tilt correction is analyzed in terms of the AoA fluctuation and collected power on the detector.