• Journal of Radiation Protection and Research
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• v.40 no.1
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• pp.1-9
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• 2015

Compton cameras overcome several limitations of conventional mechanical collimation based gamma imaging devices, such as pin-hole imaging devices, due to its electronic collimation based on coincidence logic. Especially large-scale Compton camera has wide field of view and high imaging sensitivity. Those merits suggest that a large-scale Compton camera might be applicable to monitoring nuclear materials in large facilities without necessity of portability. To that end, our research group have made an effort to design a large-scale Compton camera for safeguard application. Energy resolution or position resolution of large-area detectors vary with configuration style of the detectors. Those performances directly affect the image quality of the large-scale Compton camera. In the present study, a series of Geant4 Monte Carlo simulations were performed in order to examine the effect of those detector parameters. Performance of the designed large-scale Compton camera was also estimated for various monitoring condition with realistic modeling. The conclusion of the present study indicates that the energy resolution of the component detector is the limiting factor of imaging resolution rather than the position resolution. Also, the designed large-scale Compton camera provides the 16.3 cm image resolution in full width at half maximum (angular resolution: $9.26^{\circ}$) for the depleted uranium source considered in this study located at the 1 m from the system when the component detectors have 10% energy resolution and 7 mm position resolution.

• Journal of Broadcast Engineering
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• v.23 no.5
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• pp.614-621
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• 2018

This paper proposes a method of restoring corrupted depth image captured by depth camera through unsupervised learning using generative adversarial network (GAN). The proposed method generates restored face depth images using 3D morphable model convolutional neural network (3DMM CNN) with large-scale CelebFaces Attribute (CelebA) and FaceWarehouse dataset for training deep convolutional generative adversarial network (DCGAN). The generator and discriminator equip with Wasserstein distance for loss function by utilizing minimax game. Then the DCGAN restore the loss of captured facial depth images by performing another learning procedure using trained generator and new loss function.

• IEIE Transactions on Smart Processing and Computing
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• v.6 no.4
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• pp.262-268
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• 2017

Intelligent video surveillance systems have been developed to monitor global areas and find specific target objects using a large-scale database. However, person re-identification presents some challenges, such as pose change and occlusions. To solve the problems, this paper presents an improved person re-identification method using sparse representation and saliency-based dictionary construction. The proposed method consists of three parts: i) feature description based on salient colors and textures for dictionary elements, ii) orthogonal atom selection using cosine similarity to deal with pose and viewpoint change, and iii) measurement of reconstruction error to rank the gallery corresponding a probe object. The proposed method provides good performance, since robust descriptors used as a dictionary atom are generated by weighting some salient features, and dictionary atoms are selected by reducing excessive redundancy causing low accuracy. Therefore, the proposed method can be applied in a large scale-database surveillance system to search for a specific object.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.42 no.2
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• pp.171-178
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• 2009

The purpose of this study was the development of a digital water color measurement system using a CCD optical device. Photographs of the standard medium of Forel and Ule water color scales in the laboratory, and one of sea surface above a Secchi disc (Z=SD/2) immersed in seawater were taken. The colors of these pictures were estimated with the value of CIE $L^*a^*b^*$. Water color assessment was conducted with a digital photo-image. In the case of the Forel scale (No. 1-11), the $b^*$ value was so high that the water color number was large. In the Ule scale (No.11-21), the $a^*$ value became high, which is why the number on the water color scale was large. The color of these pictures showed that the $a^*$ value in the Forel scale and the $b^*$ value in the Ule scale increase with the increase of the F value. The $a^*$ value of seawater color was always lower than the one on the Forel and Ule water color scales. This indicates that the color of the scales differs from actual seawater color. It was concluded that water color number can more effectively be assessed by estimating the ${\Delta}Eab^*$ color difference between the water scales and actual seawater color.

• The KIPS Transactions:PartB
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• v.10B no.7
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• pp.803-810
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• 2003

In this paper, we describe a fast image mosaic method for constructing a large-scale image with video image captured from cameras that are arranged in radial shape. In the first step, we adopt the phase correlation algorithm to estimate the horizontal and vertical displacement between two adjacent images. Secondly, we calculate the accurate transform matrix among those cameras with Levenberg-Marquardt method. In the last step, those images are stitched into one large scale image in real-time by applying the transform matrix to the texture mapping function of DirectX. The feature of the method is that we do not need to use special hardware devices or write machine-level programs for Implementing a real-time mosaic system since we use conventional graphic APIs (Application Programming Interfaces), DirectX for image synthesis process.

• The Journal of Korea Robotics Society
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• v.16 no.3
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• pp.238-244
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• 2021

Image mosaicking is a common and useful technique to visualize a global map by stitching a large number of local images obtained from visual surveys in underwater environments. In particular, visual inspection of underwater structures using underwater robots can be a potential application for image mosaicking. Feature-based pairwise image registration is a commonly employed process in most image mosaicking algorithms to estimate visual odometry information between compared images. However, visual features are not always uniformly distributed on the surface of underwater structures, and thus the performance of image registration can vary significantly, which results in unnecessary computations in image matching for poor-conditioned image pairs. This study proposes a pairwise registrability measure to select informative image pairs and to improve the overall computational efficiency of underwater image mosaicking algorithms. The validity and effectiveness of the image mosaicking algorithm considering the pairwise registrability are demonstrated using an experimental dataset obtained with a full-scale ship in a real sea environment.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.9 no.2
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• pp.129-135
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• 2015

The objective grading system for the facial palsy is needed. In this study, the facial palsy grading system was developed with combination of three dimensional image processing and Nottingham scale. The developed system is composed of 4 parts; measurement part, image processing part, computational part, facial palsy evaluation & display part. Two web cam were used to get images. The 8 marker on face were recognized at image processing part. The absolute three dimensional positions of markers were calculated at computational part. Finally, Nottingham scale was calculated and displayed at facial palsy evaluation & display part. The effects of measurement method and position of subject on Nottingham scale were tested. The markers were measured with 2-dimension and 3-dimension. The subject was look at the camera with $0^{\circ}$ and $11^{\circ}$ rotation. The change of Scale was large in the case of $11^{\circ}$ rotation with 2-dimension measurement. So, the developed system with 3-dimension measurement is robust to the orientation change of subject. The developed system showed the robustness of grading error originated from subject posture.

• Journal of the Semiconductor & Display Technology
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• v.19 no.1
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• pp.6-10
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• 2020

Unlike modern photography technology, in the early days, efforts to physically compose an image with a concept similar to the current photograph have not been popular or commercially successful. The limitation of the use of images as artistic media or recordings has reached the stage of introducing the technology of image analysis to automate the function that humans recognize and judge through vision. In addition, the accuracy of the image has exceeded the human visual ability, enabling the technology that enables the step of recognizing and informing the fact that the human is not aware of it. Based on such a base, the range that can be applied through the image data in the future era can be said to be unpredictable, and the technology that targets large scale image database instead of an image is also expanding the possibilities as a new application technology. In order to identify a particular image from a massive database, different methodologies have been introduced. In this paper, we discuss image identifier production methods based on multi-resolution pyramid.

• Journal of Korea Water Resources Association
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• v.43 no.2
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• pp.153-165
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• 2010

Large-Scale Particle Image Velocimetry (LSPIV) is an extension of particle image velocimetry (PIV) for measurement of flows spanning large areas in laboratory or field conditions. LSPIV is composed of six elements - seeding, illumination, recording, image transformation, image processing, postprocessing - based on PIV. Possible error elements at each step of Mobile LSPIV (MLSPIV), which is a mobile version of LSPIV, in field applications are identified and summarized the effect of the errors which were quantified in the previous studies. The total number of elemental errors is 27, and five error sources were evaluated previously, seven elemental errors are not effective to the current MLSPIV system. Among 15 elemental errors, four errors - sampling time, image resolution, tracer, and wind - are investigated through an experiment at a laboratory to figure out how those errors affect to velocity calculation. The analysis to figure out the effect of the number of images used for image processing on the velocity calculation error shows that if over 50 images or more are used, the error due to it goes below 1 %. The effect of the image resolution on velocity calculation was investigated through various image resolution using digital camera. Low resolution image set made 3 % of velocity calculation error comparing with high resolution image set as a reference. For the effect of tracers and wind, the wind effect on tracer is decreasing remarkably with increasing the flume bulk velocity. To minimize the velocity evaluation error due to wind, tracers with high specific gravity is favorable.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.969-970
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• 2008

This paper proposes illumination-efficient iris image capturing method. face capture method for extraction of iris pattern with one high resolution camera needs a large scale near infrared illumination. we replace high-resolution camera with co-optic-axial combination between wide view camera and narrow view camera. because this method needs small scale illumination that illuminate only narrow camera viewing angle, we can capture iris pattern image with small scale near infrared illumination.