• 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.

• Journal of the Optical Society of Korea
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• v.19 no.1
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• pp.102-111
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• 2015

This paper proposes an algorithm for recovering the colors of objects from multiple near-infrared (near-IR) images. The International Commission on Illumination (CIE) color coordinates of objects are recovered from a series of gray images captured under multiple spectral near-IR illuminations using polynomial regression. The feasibility of the proposed algorithm is tested experimentally by using 24 color patches of the Color Rendition Chart. The experimental apparatus is composed of a monochrome digital camera without an IR cut-off filter and a custom-designed LED illuminator emitting multiple spectral near-IR illuminations, with peak wavelengths near the red edge of the visible band, namely at 700, 740, 780, and 860 nm. The average color difference between the original and the recovered colors for all 24 patches was found to be 11.1. However, if some particular patches with high value are disregarded, the average color difference is reduced to 4.2, and this value is within the acceptability tolerance for complex image on the display.

• Journal of Internet Computing and Services
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• v.14 no.2
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• pp.45-51
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• 2013

In this paper, the combination of ultraviolet and infrared sensors based design for flame signal detection algorithms was proposed with the application of light-wavelength from burning. And, the performance result of image detection was compared by an ultraviolet sensor, an infrared sensor, and the proposed dual-mode sensors(combination of ultraviolet and infrared sensors).

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.5
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• pp.437-443
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• 2015

This paper presents face recognition based on the fusion of visible image and thermal infrared (IR) texture estimated from the face image in the visible spectrum. The proposed face recognition scheme uses a multi- layer neural network to estimate thermal texture from visible imagery. In the training process, a set of visible and thermal IR image pairs are used to determine the parameters of the neural network to learn a complex mapping from a visible image to its thermal texture in the low-dimensional feature space. The trained neural network estimates the principal components of the thermal texture corresponding to the input visible image. Extensive experiments on face recognition were performed using two popular face recognition algorithms, Eigenfaces and Fisherfaces for NIST/Equinox database for benchmarking. The fusion of visible image and thermal IR texture demonstrated improved face recognition accuracies over conventional face recognition in terms of receiver operating characteristics (ROC) as well as first matching performances.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.100-103
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• 2008

COMS(Communication, Ocean and Meteorological Satellite) has multiple payloads; Meteorological Image(MI), Ocean Color Imager(GOCI) and Ka-band communication payloads. MI has 4 IR and 1 visible channel. In order to improve the quality of IR image, two calibration sources are used; black body image and cold space look data. In case of COMS, the space look is performed at 10.4 degree away from the nadir in east/west direction. During space look, SUN or moon intrusions are strictly forbidden, because it would degrade the quality of collected IR channel calibration data. Therefore we shall pay attention to select space look side depending on SUN and moon location. This paper proposes and discusses a simple and complete space look side selection logic based on SUN and moon intrusion event file. Computer simulation has been performed to analyze the performance of the proposed algorithm in term of east/west angular distance between space look position and hazardous intrusion sources; SUN and moon.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.174-177
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• 2009

In object tracking, the template matching methods have been developed and frequently used. It is fast enough, but not robust to an object with the variation of size and shape. In order to overcome the limitation of the template matching method, this paper proposes a template update technique. After finding an object position using the correlation-based adaptive predictive search, the proposed method selects blocks which contain object's boundary. It estimates the motion of boundary using block matching, and then updates template. We applied it to IR image sequences including an approaching object. From the experimental results, the proposed method showed successful performance to track object.

• Journal of the Korea Furniture Society
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• v.17 no.3
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• pp.79-85
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• 2006

Temperature of board surface was monitored during drying using an IR image measurement system. Boards were water-saturated and dried at the levels of four temperatures and three air velocities. At higher DB the surface temperature increased more steeply and level off period was significantly short. At the DB temperatures of 70, 80, $90^{\circ}C$ the period where the surface temperature was equivalent to WB temperature was constant regardless of air velocity while at $60^{\circ}C$ it decreased as air velocity increased. It was confirmed that a surface transfer coefficient increased with DB temperature. Variation of temperature profile on a wood surface increased with DB temperature and air velocity.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.7A
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• pp.545-551
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• 2012

In this paper, K-band multi-channel receiver was designed and fabricated for low noise amplification and down conversion to L-band. The fabricated multi-channel receiver incorporates GaAs-HEMT LNA(Low noise amplifier) which provides less than a 2 dB noise figure, IR(Image Rejection) Filter for rejection of image frequency, IR(Image rejection) mixer to reject a image frequency and improve an IMD(Intermodulation Distortion) characteristic. Test results of the fabricated multi-channel receiver show less than a 3.8 dB noise figure, conversion gain of more than 27dB, and IP1dB(Input 1dB Gain Compression Point) of -9.5 dB and over.

• Journal of the Korean Society for Precision Engineering
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• v.27 no.2
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• pp.70-77
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• 2010

This study proposed an auto focusing method for a multi-focus image in assembling lens modules in digital camera phones. A camera module in a camera phone is composed of a lens barrel, an IR glass, a lens mount, a PCB board and aspheric lenses. Alignment among the components is one of the important factors in product quality. Auto-focus is essential to adjust image quality of an IR glass in a lens holder, but there are two focal points in the captured image due to thickness of IR glass. So, sharpness, probability and a scale factor are defined to find desired focus from a multi-focus image. The sharpness is defined as clarity of an image. Probability and a scale factors are calculated using pattern matching with a registered image. The presented algorithm was applied to a lens assembly machine which has 5 axes, two vacuum chucks and an inspection system. The desired focus can be determined on the local maximum of the sharpness, the probability and the scale factor in the experiment.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.6
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• pp.493-497
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• 2016

Infrared (IR) imaging is capable of detecting targets that are not visible at night, thus it has been widely used for the security and defense system. However, the quality of the IR image is often degraded by low resolution and noise corruption. This paper addresses target segmentation with the IR image. Multiple regions of interest (ROI) are extracted by the multi-level segmentation and targets are segmented from the individual ROI. Each level of the multi-level segmentation is composed of a k-means clustering algorithm an expectation-maximization (EM) algorithm, and a decision process. The k-means clustering algorithm initializes the parameters of the Gaussian mixture model (GMM) and the EM algorithm iteratively estimates those parameters. Each pixel is assigned to one of clusters during the decision. This paper proposes the selection and the merging of the extracted ROIs. ROI regions are selectively merged in order to include the overlapped ROI windows. In the experiments, the proposed method is tested on an IR image capturing two pedestrians at night. The performance is compared with conventional methods showing that the proposed method outperforms others.