• Proceedings of the KOSOMBE Conference
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• v.1997 no.11
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• pp.357-360
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• 1997

For medical students and doctors, knowledge of the 3-dimensional (3D) structure of the heart is very important in diagnosis and treatment of the heart diseases. 2-dimensional (2D) tools (e.g. anatomy book) or classical 3D tools (e.g. plastic model) are not sufficient or understanding the complex structures of the heart. Moreover, it is not always guaranteed to dissect the heart of cadaver when it is necessary. To overcome this problem, virtual dissection systems of the heart have been developed. But these systems are not satisfactory since they are made of radiographs; they are not true 3D images; they can not be used to dissect freely; or they can only be operated on the workstation. It is also necessary to make the dissection systems incorporating the various races and tribes because of the organ's difference according to race and tribe. This study was intended to make the 3D image of the heart from a Korean cadaver, and to establish a virtual dissection system of the heart with a personal computer. The procedures or manufacturing this system were as follows. 1. The heart from a Korean adult cadaver was embedded with gelatin solution, and serially cross-sectioned at 1mm-thickness on a meat slicer. Pictures or 153 cross-sectioned specimens were inputted into the computer using a digital camera ($756{\times}504$ resolution, true color). 2. The alignment system was established by means of the language of IDL, and applied to align 2D images of the heart. In each of 2D images, closed curves lining clean and dirty blood pathways were drawn manually on the CorelDRAW program. 3. Using the language of IDL, the 3D image and the virtual dissection system of the heart were constructed. The virtual dissection system of the heart allowed or ree rotation, any-directional sectioning, and selected visualization of the heart's structure. This system is expected to become more advanced, and to be used widely through Internet or CD-title as an educational tool for medical students and doctors.

• Korean Journal of Optics and Photonics
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• v.28 no.4
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• pp.153-157
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• 2017

An infrared camera and laser common-path optical system is applied to DIRCM (directional infrared countermeasures), to increase boresighting accuracy and decrease weight. Thermal effects of a laser beam in a common-path optical system are analyzed and evaluated, to predict any degradation in image quality. A laser beam with high energy density is absorbed by and heats the optical components, and then the surface temperature of the optical components increases. The heated optical components of the common-path optical system decrease system transmittance, which can degrade image quality. For analysis, the assumed simulation condition is that the laser is incident for 10 seconds on the mirror (aluminum, silica glass, silicon) and lens (sapphire, zinc selenide, silicon, germanium) materials, and the surface temperature distribution of each material is calculated. The wavelength of the laser beam is $4{\mu}m$ and its output power is 3 W. According to the results of the calculations, the surface temperature of silica glass for the mirror material and sapphire for the lens material is higher than for other materials; the main reason for the temperature increase is the absorption coefficient and thermal conductivity of the material. Consequently, materials for the optical components with high thermal conductivity and low absorption coefficient can reduce the image-quality degradation due to laser-beam thermal effects in an infrared camera and laser common-path optical system.

• Investigative Magnetic Resonance Imaging
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• v.19 no.2
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• pp.107-113
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• 2015

Purpose: Susceptibility weighted imaging (SWI) is a new magnetic resonance technique that can exploit the magnetic susceptibility differences of various tissues. Intracranial hemorrhage (ICH) looks a dark blooming on the magnitude images of SWI. However, the pattern of ICH on phase images is not well known. The purpose of this study is to characterize hemorrhagic lesions on the phase images of SWI. Materials and Methods: We retrospectively enrolled patients with ICH, who underwent both SWI and precontrast CT, between 2012 and 2013 (n = 95). An SWI was taken, using the 3-tesla system. A phase map was generated after postprocessing. Cases with an intracranial hemorrhage were reviewed by an experienced neuroradiologist and a trainee radiologist, with 10 years and 3 years of experience, respectively. The types and stages of the hemorrhages were determined in correlation with the precontrast CT, the T1- and T2-weighted images, and the FLAIR images. The size of the hemorrhage was measured by a one- directional axis on a magnitude image of SWI. The phase values of the ICH were qualitatively evaluated: hypo-, iso-, and hyper-intensity. We summarized the imaging features of the intracranial hemorrhage on the phase map of the SWI. Results: Four types of hemorrhage are observed: subdural and epidural; subarachnoid; parenchymal hemorrhage; and microbleed. The stages of the ICH were classified into 4 groups: acute (n = 34); early subacute (n = 11); late subacute (n = 15); chronic (n = 8); stage-unknown microbleeds (n = 27). The acute and early subacute hemorrhage showed heterogeneous mixed hyper-, iso-, and hypo-signal intensity; the late subacute hemorrhage showed homogeneous hyper-intensity, and the chronic hemorrhage showed a shrunken iso-signal intensity with the hyper-signal rim. All acute subarachnoid hemorrhages showed a homogeneous hyper-signal intensity. All parenchymal hemorrhages (> 3 mm) showed a dipole artifact on the phase images; however, microbleeds of less than 3 mm showed no dipole artifact. Larger hematomas showed a heterogeneous mixture of hyper-, iso-, and hypo-signal intensities. Conclusion: The pattern of the phase value of the SWI showed difference, according to the type, stage, and size.

• The Journal of the Petrological Society of Korea
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• v.17 no.4
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• pp.206-221
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• 2008

We have studied general orientational characteristics of microcracks distributed in Bulgugsa Granites of southwestern Gyeongsang Basin. Microcracks of 131 sets, which were developed on horizontal surfaces of II rock samples collected from Sacheon-Gosung, Geoje-si and Namhae-gun areas, were distinguished by image processing. Then, 45 sets with a distinct linear array on image were sorted out. These microcracks can be comparable with vertical grain planes. Orientations of these microcracks were compared with those of vertical rift and grain planes developed in Cretaceous and Jurassic granites of Korea. In the distribution chart, the agreement of the distribution pattern between microcracks of 45 sets and above vertical planes suggests that microcrack systems developed all over the study area also occur regionally in Cretaceous and Jurassic granites of Korea. Whole domain of the directional angle-frequency chart can be divided into 20 domains in terms of the phases of the distribution of microcracks. Meanwhile, 18 domains from 45 sets of microcracks were compared with the maximum principal stress orientations suggested from previous studies. The majority of maximum principal stress orientations pertain to domain $1{\sim}2$, $5{\sim}6$, $11{\sim}15$, $17{\sim}18$ and $19{\sim}20$, and these domains are coincident with the orientation of the 1st and 2nd-frequency orders represented in a rose diagram for 45 sets of microcracks. Representative orientations of open microcrack reflect the maximum principal stress orientations suggested in previous studies.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.7
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• pp.1380-1386
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• 2007

This paper proposed an automatic recognition system of shipping container identifiers using fuzzy-based noise removal method and ART2-based self-organizing supervised learning algorithm. Generally, identifiers of a shipping container have a feature that the color of characters is blacker white. Considering such a feature, in a container image, all areas excepting areas with black or white colors are regarded as noises, and areas of identifiers and noises are discriminated by using a fuzzy-based noise detection method. Areas of identifiers are extracted by applying the edge detection by Sobel masking operation and the vertical and horizontal block extraction in turn to the noise-removed image. Extracted areas are binarized by using the iteration binarization algorithm, and individual identifiers are extracted by applying 8-directional contour tacking method. This paper proposed an ART2-based self-organizing supervised learning algorithm for the identifier recognition, which improves the performance of learning by applying generalized delta learning and Delta-bar-Delta algorithm. Experiments using real images of shipping containers showed that the proposed identifier extraction method and the ART2-based self-organizing supervised learning algorithm are more improved compared with the methods previously proposed.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.1-25
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• 2020

In this paper, we suggest an application system architecture which provides accurate, fast and efficient automatic gasometer reading function. The system captures gasometer image using mobile device camera, transmits the image to a cloud server on top of private LTE network, and analyzes the image to extract character information of device ID and gas usage amount by selective optical character recognition based on deep learning technology. In general, there are many types of character in an image and optical character recognition technology extracts all character information in an image. But some applications need to ignore non-of-interest types of character and only have to focus on some specific types of characters. For an example of the application, automatic gasometer reading system only need to extract device ID and gas usage amount character information from gasometer images to send bill to users. Non-of-interest character strings, such as device type, manufacturer, manufacturing date, specification and etc., are not valuable information to the application. Thus, the application have to analyze point of interest region and specific types of characters to extract valuable information only. We adopted CNN (Convolutional Neural Network) based object detection and CRNN (Convolutional Recurrent Neural Network) technology for selective optical character recognition which only analyze point of interest region for selective character information extraction. We build up 3 neural networks for the application system. The first is a convolutional neural network which detects point of interest region of gas usage amount and device ID information character strings, the second is another convolutional neural network which transforms spatial information of point of interest region to spatial sequential feature vectors, and the third is bi-directional long short term memory network which converts spatial sequential information to character strings using time-series analysis mapping from feature vectors to character strings. In this research, point of interest character strings are device ID and gas usage amount. Device ID consists of 12 arabic character strings and gas usage amount consists of 4 ~ 5 arabic character strings. All system components are implemented in Amazon Web Service Cloud with Intel Zeon E5-2686 v4 CPU and NVidia TESLA V100 GPU. The system architecture adopts master-lave processing structure for efficient and fast parallel processing coping with about 700,000 requests per day. Mobile device captures gasometer image and transmits to master process in AWS cloud. Master process runs on Intel Zeon CPU and pushes reading request from mobile device to an input queue with FIFO (First In First Out) structure. Slave process consists of 3 types of deep neural networks which conduct character recognition process and runs on NVidia GPU module. Slave process is always polling the input queue to get recognition request. If there are some requests from master process in the input queue, slave process converts the image in the input queue to device ID character string, gas usage amount character string and position information of the strings, returns the information to output queue, and switch to idle mode to poll the input queue. Master process gets final information form the output queue and delivers the information to the mobile device. We used total 27,120 gasometer images for training, validation and testing of 3 types of deep neural network. 22,985 images were used for training and validation, 4,135 images were used for testing. We randomly splitted 22,985 images with 8:2 ratio for training and validation respectively for each training epoch. 4,135 test image were categorized into 5 types (Normal, noise, reflex, scale and slant). Normal data is clean image data, noise means image with noise signal, relfex means image with light reflection in gasometer region, scale means images with small object size due to long-distance capturing and slant means images which is not horizontally flat. Final character string recognition accuracies for device ID and gas usage amount of normal data are 0.960 and 0.864 respectively.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.3
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• pp.1-10
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• 2010

In this paper, we propose a novel way of combining color demosaicing and the auto white balance (AWB) method, which are important parts of image processing. Performance of the AWB is generally affected by demosaicing results because most AWB algorithms are performed posterior to color demosaicing. In order to increase the performance and efficiency of the AWB algorithm, the color constancy problem is considered during the color demosaicing step. Initial estimates of the directional luminance and chrominance values are defined for estimating edge direction and calculating the AWB gain. We propose a modified edge-based AWB method that used a pre-defined achromatic region. The estimation of edge direction is performed region adaptively by using the local statistics of the initial estimates of the luminance and chrominance information. The proposed method shows significant improvements in terms of visual and numerical criteria when compared to conventional methods.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.3
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• pp.61-69
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• 2007

This paper proposes new preprocessing algorithm to extract minutiae in the process of fingerprint recognition. Fingerprint images quality enhancement is a topic phase to ensure good performance in a topic phase to ensure good performance in a Automatic Fingerprint Identification System(AFIS) based on minutiae matching. This paper proposes an algorithm to improve fingerprint image preprocessing to extract minutiae accurately based on directional filter. We improved the suitability of low quality fingerprint images to better suit fingerprint recognition by using valid ridge vector and ridge probability of fingerprint images. With the proposed fingerprint improvement algorithm, noise is removed and presumed ridges are more clearly ascertained. The algorithm is based on five step: computation of effective ridge vector, computation of ridge probability, noise reduction, ridge emphasis, and orientation compensation and frequency estimation. The performance of the proposed approach has been evaluated on two set of images: the first one is self collected using a capacitive semiconductor sensor and second one is DB3 database from Fingerprint Verification Competition (FVC).

• Journal of the Korea Computer Graphics Society
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• v.12 no.4
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• pp.7-15
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• 2006

The ultimate objective of painterly rendering is to express an inputted image as if it is hand drawn. The factors to express this painterly effect are thickness of the brush, direction, texture and the establishment of criteria judging if the produced brush will be drawn on to the canvas. In this paper, the algorithm using density of the edges in determining the criteria whether the brush will be drawn onto the canvas is proposed. Density of edges refers to the quantity of edge in the specific area. And uses the method of finding the location of the brush to be drawn as a unit of dynamic grid as well as expressing consistent directional through direction interpolation. Also, the texture is expressed using various textured brushes. Considering density of edges,We can express detailed area and abstract area. And it result in more human effect of oil painting.

• The Journal of the Korea Contents Association
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• v.14 no.12
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• pp.545-554
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• 2014

In this paper, we propose online Chinese character learning machine with a depth camera, where a system presents a Chinese character on a screen and a user is supposed to draw the presented Chinese character by his or her hand gesture. We develop the hand tracking method and suggest the adjusted chain code to represent constituent strokes of a Chinese character. For hand tracking, a fingertip is detected and verified. The adjusted chain code is designed to contain the information on order and relative length of each constituent stroke as well as the information on the directional variation of sample points. Such information is very efficient for a real-time match process and checking incorrectly drawn parts of a stroke.