• Biomedical Science Letters
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• v.10 no.4
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• pp.367-373
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• 2004

Evaluated the hyperacute embolic effects of triolein and oleic acid in cat brains by using MR image and electron microscopy. In fat embolism, free fatty acid is more toxic than neutral fat in terms of tissue damage. T2-Weighted imaging, diffusion-weighted imaging, and contrast-enhanced T1-weighted imaging were performed in cat brains after the injection of triolein (group 1, n=8) or oleic acid (group 2, n=10) into the internal carotid artery. MR image were quantitatively assessed by comparing the lesions with their counterparts on T2-weighted images, apparent diffusion coefficient (ADC) maps, and contrast-enhanced T1-weighted images. Electron microscopic findings in group 1 were compared with those in group 2. Qualitatively, MR images revealed two types of lesions. Type 1 lesions were hyperintense on diffusion-weighted images and hypointense of ADC maps. Type 2 lesions were isointense or mildly hyperintense on diffusion-weighted images and isointense on ADC maps. Quantitatively, the signal intensity rations of type 1 lesions in group 2 specimens were significantly higher on T2-weighted images (P=.013)/(P=.027) and lower on ADC maps compared with those of group 1. Electron microscopy of type 1 lesions in both groups revealed more prominent widening of the perivascular space and swelling of the neural cells in groups 1. MR and electron microscopic data on cerebral fat embolism induced by either triolein or oleic acid revealed characteristics suggestive of both vasogenic and cytotoxic edema in the hyperacute stage. Tissue damage appeared more severe in the oleic acid group than in the triolein group.

• The KIPS Transactions:PartB
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• v.10B no.2
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• pp.179-188
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• 2003

In content-based image retrieval, the query is an image itself and the retrieval process is the process that seeking the similar images to the given query image. In this way of retrieval, the user has to know the basic physical features of target images that he wants to retrieve. But it has some restriction because to retrieve the target image he has to know the basic physical feature space such as color, texture, shape and spatial relationship. In this paper, we propose an emotion-based retrieval system. It uses the emotion that color images have. It is different from past emotion-based image retrieval in point of view that it uses relevance feedback to estimate the users intend and it is easily combined with past content-based image retrieval system. To test the performance of our proposed system, we use MPEG-7 color descriptor and emotion language such as "warm", "clean", "bright" and "delight" We test about 1500 wallpaper images and get successful result.lpaper images and get successful result.

• Journal of the Korean Society of Clothing and Textiles
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• v.16 no.4 s.44
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• pp.379-391
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• 1992

This study was intended to identify the constructing factors and the evaluative dimensions of clothing images. A questionnaire consisted of 110 words expressing clothing images was developed, and eight clothing photographs were selected as stimuli. 298 female subjects aged between 22 to 37 responsed to the 110 words for two photographs during September in 1991. After survey, 110 words were reduced to 62 words based on their independence, then factor analysis was conducted. As a result of factor analysis,6 factors-grace, modernity, unattractive- ness, activeness, dressiness, and youthfulness were found out as constructing factors of clothing images. One additional interest was the effect of design line to the formation of clothing images. ANOVA identified that curved line designs were perceived to be more graceful, modern, dressy, and youthful, and straight line designs were perceived to be more unattractive and active. The other interest was the effect of image factors to the total evaluation. So, regression was used. Consequently, the most influential factor to the total evaluation was found out as grace, followed by unattractiveness, modernity, youthfulness and activeness in a descending order. To identify the evaluative dimensions of clothing images, nine words of unattractiveness image factor were eliminated, and multidimensional scaling analysis was employed. Here, three dimensions were judged to be appropriate to explain the result. The first dimension in the multidimensional space was the evaluation in 'mannish image versus feminine image'. The second was the evaluation in 'simple image versus decorative image'. The third was the evaluation in 'pastoral image versus urbane image'.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.5
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• pp.199-207
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• 2013

Spin images representing efficiently surface features of 3D mesh models have been used to detect facial landmark points. However, at a certain point, different normal direction can lead to quite different spin images. Moreover, since 3D points are projected to the 2D (${\alpha}-{\beta}$) space during spin image generation, surface features cannot be described clearly. In this paper, we present a method to detect 3D facial landmark using improved spin images by partitioning the search area with respect to angle. By generating sub-spin images for angular partitioned 3D spaces, more unique features describing corresponding surfaces can be obtained, and improve the performance of landmark detection. In order to generate spin images robust to inaccurate surface normal direction, we utilize on averaging surface normal with its neighboring normal vectors. The experimental results show that the proposed method increases the accuracy in landmark detection by about 34% over a conventional method.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.5
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• pp.16-23
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• 2011

Muzzle flash based on gunnery is the target that has huge energy. So, gunnery target in a long range over xx km is distinguishable in the IR(infrared) images, on the other hand, is not distinguishable in the CCD images. In this paper, we propose the classification method of gunnery targets in a infrared images and in a long range. The energy from gunnery have an effect on varous pixel values in infrared images as a property of infrared image sensor, distance, and atmosphere, etc. For this reason, it is difficult to classify gunnery targets using pixel values in infrared images. In proposed method, we take the profile of pixel values using high performance infrared sensor, and classify gunnery targets using modeling GMM and shape of profile. we experiment on the proposed method with infrared images in the ground and aviation. In experimental result, the proposed method provides about 93% classification rate.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.7 no.5
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• pp.64-76
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• 2008

To increase road safety at blackspots, it is needed to develop a new method that can process before accident occurrence. Accident situation could result from traffic conflict. Traffic conflict decision technique has an advantage that can acquire and analyze data in time and confined space that is less through investigation. Therefore, traffic conflict technique is highly expected to be used in many application of road safety. This study developed traffic conflict decision program that can analyze and process from signalized intersection image. Program consists of the following functional modules: an image input module that acquires images from the CCTV camera, a Save-to-Buffer module which stores the entered images by differentiating them into background images, current images, difference images, segmentation images, and a conflict detection module which displays the processed results. The program was developed using LabVIEW 8.5 (a graphic language) and the VISION module library.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.1
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• pp.477-483
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• 2019

In this paper, we propose vanishing-point position-improvement algorithms by using multiple images and a homography matrix. Vanishing points can be detected from a single image, but the positions of detected vanishing points can be improved if we adjust their positions by using information from multiple images. More accurate indoor space information detection is possible through vanishing points with improved positional accuracy. To adjust a position, we take three images and detect the information, detect the homography matrix between the walls of the images, and convert the vanishing point positions using the detected homography. Finally, we find an optimal position among the converted vanishing points and improve the vanishing point position. The experimental results compared an existing algorithm and the proposed algorithm. With the proposed algorithm, we confirmed that the error angle to the vanishing point position was reduced by about 1.62%, and more accurate vanishing point detection was possible. In addition, we can confirm that the layout detected by using improved vanishing points through the proposed algorithm is more accurate than the result from the existing algorithm.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.11
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• pp.1474-1480
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• 2018

When fires occur in high-rise buildings, it is difficult to determine whether each escape route is safe because of complex structure. Therefore, it is necessary to provide residents with escape routes quickly after determining their safety. We propose a method to measure the visibility of the escape route due to the smoke generated in the fire by analyzing the images. The visibility can be easily measured if the density of smoke detected in the input image is known. However, this approach is difficult to use because there are no suitable methods for measuring smoke density. In this paper, we use principal component analysis by extracting a background image from input images and making it training data. Background images and smoke images are extracted from images given as inputs, and then the learned principal component analysis is applied to map of as a new feature space, and the change is calculated and the visibility due to the smoke is measured.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.1
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• pp.9-17
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• 2024

It is widely recognized that for 3D human pose estimation (HPE), dataset acquisition is expensive and the effectiveness of augmentation techniques of conventional visual recognition tasks is limited. We address these difficulties by presenting a simple but effective method that augments input images in terms of viewpoints when training a 3D human pose estimation (HPE) model. Our intuition is that meaningful variants of the input images for HPE could be obtained by viewing a human instance in the images from an arbitrary viewpoint different from that in the original images. The core idea is to synthesize new images that have self-occlusion and thus are difficult to predict at different viewpoints even with the same pose of the original example. We incorporate this idea into the training procedure of the 3D HPE model as an augmentation stage of the input samples. We show that a strategy for augmenting the synthesized example should be carefully designed in terms of the frequency of performing the augmentation and the selection of viewpoints for synthesizing the samples. To this end, we propose a new metric to measure the prediction difficulty of input images for 3D HPE in terms of the distance between corresponding keypoints on both sides of a human body. Extensive exploration of the space of augmentation probability choices and example selection according to the proposed distance metric leads to a performance gain of up to 6.2% on Human3.6M, the well-known pose estimation dataset.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.8
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• pp.97-103
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• 2014

Signal processing technique of linear frequency modulation-frequency shift keying (LFM-FSK) waveform has been introduced for rear view radars of an automobile. LFM-FSK waveform consists of two sequential stepped frequency waveforms with some frequency offset, and thus, can be used to generate inverse synthetic aperture radar (ISAR) images of rear view target of an automobile. However, ISAR images can often be blurred due to inter-pulse phase errors. To resolve this problem, one-dimensional (1-D) entropies of high resolution range profiles (HRRP) are minimized with the help of particle swarm optimization (PSO). The searching space used in PSO is adaptively adjusted by the use of information on the target's velocity obtained from LFM-FSK waveforms. Simulation results show that the proposed method can generate well-focused ISAR images.