• Journal of information and communication convergence engineering
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• v.13 no.4
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• pp.280-285
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• 2015

Double random phase encryption (DRPE) is one of the well-known optical encryption techniques, and many techniques with DRPE have been developed for information security. However, most of these techniques may not solve the fundamental security problem caused by using fixed phase masks for DRPE. Therefore, in this paper, we propose a key phase mask updating scheme for DRPE to improve its security, where a spatial light modulator (SLM) is used to implement key phase mask updating. In the proposed scheme, updated key data are obtained by using previous image data and the first phase mask used in encryption. The SLM with the updated key is used as the second phase mask for encryption. We provide a detailed description of the method of encryption and decryption for a DRPE system using the proposed key updating scheme, and simulation results are also shown to verify that the proposed key updating scheme can enhance the security of the original DRPE.

• Journal of the Semiconductor & Display Technology
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• v.10 no.4
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• pp.79-87
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• 2011

In this paper, suggested is a hybrid-type visual alignment system to align mask and panel in 3-D space, where mask and panel are to be controlled independently by two individual positioning mechanisms in order to compensate for spatial misalignments. In the hybrid visual alignment system, the below 4-PPR parallel mechanism provides in-plain motions to pattern mask like the other conventional alignment systems while the above 4-RPS parallel mechanism is to move glass panel to achieve a complete spatial alignment. For the control of the hybrid alignment system, first, inverse kinematic solutions for the parallel mechanisms are given to determine the driving distance of each active joint, and also an efficient way to determine the spatial alignment error is developed by exploiting three in-plane cameras.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.251-256
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• 1993

In KACC'91 and '92 conference, we proposed a method of automatically detecting the shape of the faulty holes in a shadow mask by use of CCD ca.mera and image data processing technic. In this method, two adjoining test areas from one image data. of the shadow mask are taken and comparing the shape of holes in these two areas, we can detect the faults in the shadow mask. In this paper, a method is described by use of spatial filtering of effectively finding the faulty holes from the difference image data between the two tested image data. The main role of the filter is to remove sampling errors occurring at the edge of the holes. And the second role is not only to find the existence of faulty holes but also exactly express the shape of faulty holes. Computer simulations and actual experiments with shadow masks have shown that this method of fault detection is very effective for practical use.

• Journal of the Korea Society of Computer and Information
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• v.25 no.5
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• pp.37-46
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• 2020

Recently, studies of facial attribute editing have obtained realistic results using generative adversarial net (GAN) and encoder-decoder structure. Spatial attention GAN (SaGAN), one of the latest researches, is the method that can change only desired attribute in a face image by spatial attention mechanism. However, sometimes unnatural results are obtained due to insufficient information on face areas. In this paper, we propose an improved SaGAN (MSaGAN) using a guide mask for learning and applying multitask learning approach to improve the limitations of the existing methods. Through extensive experiments, we evaluated the results of the facial attribute editing in therms of the mask loss function and the neural network structure. It has been shown that the proposed method can efficiently produce more natural results compared to the previous methods.

• Journal of Korea Multimedia Society
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• v.23 no.1
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• pp.8-14
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• 2020

For a reliable fingerprinting system, improving fingerprint matching accuracy is crucial. In this paper, we try to improve a binary image fingerprint matching performance by utilizing auxiliary information, power mask, which is obtained while constructing fingerprint DB. The power mask is an expected robustness of each fingerprint bit. A caveat of the power mask is the increased storage cost of the fingerprint DB. This paper mitigates the problem by reducing the size of the power mask utilizing spatial correlation of an image. Experiments on a publicly-available image dataset confirmed that the power mask is effective in improving fingerprint matching accuracy.

• Journal of the Optical Society of Korea
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• v.12 no.3
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• pp.192-195
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• 2008

We report on the spatial information of a coupling laser transfer into a reflected probe laser; the transfer is achieved by means of electromagnetically induced transparency through a common excited state connected with $5S_{1/2}-5P_{1/2}(F=1{\rightarrow}F'=2)$ in the $^{87}Rb\;D_1$ line. When the coupling laser was spatially modulated as a stationary wave, the absorption of the probe laser was enhanced and the reflection of the probe laser was generated. When the coupling laser was spatially modulated by a mask, we observed that the reflection light of the probe laser was modulated as the shape of the mask. The Bragg reflection transferred the spatial information of the coupling laser. The reflection was approximately 7% of the incident power of the probe laser.

• Smart Structures and Systems
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• v.25 no.3
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• pp.301-310
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• 2020

The laser ultrasonic technique is gaining popularity for nondestructive evaluation (NDE) applications because it is a noncontact and couplant-free method and can inspect a target from a remote distance. For the conventional laser ultrasonic techniques, a pulsed laser is often used to generate broadband ultrasonic waves in a target structure. However, for crack detection using nonlinear ultrasonic modulation, it is necessary to generate narrowband ultrasonic waves. In this study, a pulsed laser is shaped into dual-line arrays using a spatial mask and used to simultaneously excite narrowband ultrasonic waves in the target structure at two distinct frequencies. Nonlinear ultrasonic modulation will occur between the two input frequencies when they encounter a fatigue crack existing in the target structure. Then, a nonlinear damage index (DI) is defined as a function of the magnitude of the modulation components and computed over the target structure by taking advantage of laser scanning. Finally, the fatigue crack is detected and localized by visualizing the nonlinear DI over the target structure. Numerical simulations and experimental tests are performed to examine the possibility of generating narrowband ultrasonic waves using the spatial mask. The performance of the proposed fatigue crack localization technique is validated by conducting an experiment with aluminum plates containing real fatigue cracks.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.3
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• pp.275-284
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• 2023

Land monitoring involves systematically understanding changes in land use, leveraging spatial information such as satellite imagery and aerial photographs. Recently, the integration of deep learning technologies, notably object detection and semantic segmentation, into land monitoring has spurred active research. This study developed a web service to facilitate such integrations, allowing users to analyze aerial and drone images using CNN models. The web service architecture comprises AI, WEB/WAS, and DB servers and employs three primary deep learning models: DeepLab V3, YOLO, and Rotated Mask R-CNN. Specifically, YOLO offers rapid detection capabilities, Rotated Mask R-CNN excels in detecting rotated objects, while DeepLab V3 provides pixel-wise image classification. The performance of these models fluctuates depending on the quantity and quality of the training data. Anticipated to be integrated into the LX Corporation's operational network and the Land-XI system, this service is expected to enhance the accuracy and efficiency of land monitoring.

• Progress in Medical Physics
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• v.19 no.4
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• pp.247-255
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• 2008

A coded aperture camera has been developed to improve the signal-to-noise ratio (SNR) while keeping the spatial resolution of a pinhole gamma camera. The purpose of this study was to optimize a coded aperture camera and to evaluate its possibility for thyroid imaging by Monte Carlo simulation. A clinical gamma camera, a pinhole collimator with 1.0 mm hole diameter, and a $79{\times}79$ modified uniformly redundant array (MURA) mask were designed using GATE (Geant4 Application for Tomographic Emission). The penetration ratio, spatial resolution, integral uniformity and signal-to-noise ratio (SNR) were simulated and evaluated as a function of the mask thickness. The spatial resolution of the coded aperture camera was consistent with the various mask thickness, SNR showed a maximum value at 1.2 mm mask thickness and integral uniformity was improved by increasing mask thickness. Compare to the pinhole gamma camera, the coded aperture camera showed improved SNR by a factor of 30 while keeping almost the same spatial resolution. In this simulation study, the results indicated that high spatial resolution and ultra-high SNR of the thyroid imaging are feasible using a coded aperture camera.

• Journal of Cadastre & Land InformatiX
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• v.48 no.2
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• pp.213-225
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• 2018

For constructing three-dimensional (3D) spatial information occlusion region problem arises in the process of taking the texture of the building. In order to solve this problem, it is necessary to investigate the automation method to automatically recognize the occlusion region, issue it, and automatically complement the texture. In fact there are occasions when it is possible to generate a very large number of structures and occlusion, so alternatives to overcome are being considered. In this study, we attempt to apply an approach to automatically create an occlusion region based on learning by patterning the blocked region using the recently emerging deep learning algorithm. Experiment to see the performance automatic detection of people, banners, vehicles, and traffic lights that cause occlusion in building walls using two advanced algorithms of Convolutional Neural Network (CNN) technique, Faster Region-based Convolutional Neural Network (R-CNN) and Mask R-CNN. And the results of the automatic detection by learning the banners in the pre-learned model of the Mask R-CNN method were found to be excellent.