• Journal of The Korean Astronomical Society
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• v.42 no.3
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• pp.61-69
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• 2009

Transverse velocity vectors can be determined from a pair of images successively taken with a time interval using an optical flow technique. We have tested the performance of the new technique called NAVE (non-linear affine velocity estimator) recently implemented by Chae & Sakurai using real image data taken by the Narrowband Filter Imager (NFI) of the Solar Optical Telescope (SOT) aboard the Hinode satellite. We have developed two methods of estimating the errors in the determination of velocity vectors, one resulting from the non-linear fitting ${\sigma}_{\upsilon}$ and the other ${\epsilon}_u$ resulting from the statistics of the determined velocity vectors. The real error is expected to be somewhere between ${\sigma}_{\upsilon}$ and ${\epsilon}_u$. We have investigated the dependence of the determined velocity vectors and their errors on the different parameters such as the critical speed for the subsonic filtering, the width of the localizing window, the time interval between two successive images, and the signal-to-noise ratio of the feature. With the choice of $v_{crit}$ = 2 pixel/step for the subsonic filtering, and the window FWHM of 16 pixels, and the time interval of one step (2 minutes), we find that the errors of velocity vectors determined using the NAVE range from around 0.04 pixel/step in high signal-to-noise ratio features (S/N $\sim$ 10), to 0.1 pixel/step in low signa-to-noise ratio features (S/N $\sim$ 3) with the mean of about 0.06 pixel/step where 1 pixel/step corresponds roughly to 1 km/s in our case.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.6C
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• pp.349-354
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• 2011

Passive millimeter wave imaging has the capability of detecting concealed objects under clothing. Also, passive millimeter imaging can obtain interpretable images under low visibility conditions like rain, fog, smoke, and dust. However, the image quality is often degraded due to low spatial resolution, low signal level, and low temperature resolution. This paper addresses image registration and fusion between passive millimeter images and visual images. The goal of this study is to combine and visualize two different types of information together: human subject's identity and concealed objects. The image registration process is composed of body boundary detection and an affine transform maximizing cross-correlation coefficients of two edge images. The image fusion process comprises three stages: discrete wavelet transform for image decomposition, a fusion rule for merging the coefficients, and the inverse transform for image synthesis. In the experiments, various types of metallic and non-metallic objects such as a knife, gel or liquid type beauty aids and a phone are detected by passive millimeter wave imaging. The registration and fusion process can visualize the meaningful information from two different types of sensors.

• Proceedings of the Korean Aquaculture Society Conference
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• 2003.10a
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• pp.132-133
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• 2003

The marine dinoflagellate genus Alexandrium comprise PSP producing A. acatenella, A. angustitabuzatum, A. catenella, A. fundyense, A. minutum, A. ostenfezdii, A. tamiyavanichii and A. tamarense. In monitoring toxic Alexandrium, rapid and exact species identification is one of the significant prerequisite work, however we have suffered confusion of species definition in Alexandrium. To surmount this problem, we chose DNA probing, which has long been used as an alternative for conventional identification methods, primarily relying on morphological approaches using microscope in microbial field. Oligonucleotide DNA probes targeting rRNA or rDNA have been commonly used in diverse studies to detect and enumerate cells concerned as a culture-indetendent powerful tool. Despite of the massive literature on the HAB species containing Alexandrium, application of DNA probing for species identification and detection has been limited to a few documents. DNA probes of toxic A. tamarense, A. catenella and A. tamiyavanichii, and non-toxic A. affine, A. fraterculus, A. insuetum and A. pseudogonyaulax were designed from LSU rDNA D1-D2, and applied to whole cell-FISH. Each DNA probes reacted only the targeted Alexandrium cells with very high species-specificity within Alexandrium. The probes could detect each targeted cells obtained from the natural sea water samples without cross-reactivity. Labeling intensity varied in the growth stage, this showed that the contents of probe-targeted cellular rRNA decreased with reduced growth rate. Double probe TAMID2S1 achieved approximately two times higher fluorescent intensity than that with single probe TAMID2. This double probe did not cross-react with any kinds of microorganisms in the natural sea waters. Therefore we can say that in whole-cell FISH procedure this double DNA probe successfully labeled targeted A. tamiyavanichii without cross-reaction with congeners and diverse natural bio-communities.

• Journal of Korea Multimedia Society
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• v.25 no.1
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• pp.1-9
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• 2022

Ship detection in synthetic aperture radar (SAR) images is an important application in marine monitoring for the military and civilian domains. Over the past decade, object detection has achieved significant progress with the development of convolutional neural networks (CNNs) and lot of labeled databases. However, due to difficulty in collecting and labeling SAR images, it is still a challenging task to solve SAR ship detection CNNs. To overcome the problem, some methods have employed conventional data augmentation techniques such as flipping, cropping, and affine transformation, but it is insufficient to achieve robust performance to handle a wide variety of types of ships. In this paper, we present a novel and effective approach for deep SAR ship detection, that exploits label-rich Electro-Optical (EO) images. The proposed method consists of two components: a data augmentation network and a ship detection network. First, we train the data augmentation network based on conditional generative adversarial network (cGAN), which aims to generate additional SAR images from EO images. Since it is trained using unpaired EO and SAR images, we impose the cycle-consistency loss to preserve the structural information while translating the characteristics of the images. After training the data augmentation network, we leverage the augmented dataset constituted with real and translated SAR images to train the ship detection network. The experimental results include qualitative evaluation of the translated SAR images and the comparison of detection performance of the networks, trained with non-augmented and augmented dataset, which demonstrates the effectiveness of the proposed framework.

• Progress in Medical Physics
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• v.21 no.2
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• pp.153-164
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• 2010

To determine the clinical target volumes considering vascularity and cellularity of tumors, the software was developed for mapping of the analyzed biological clinical target volumes on anatomical images using regional cerebral blood volume (rCBV) maps and apparent diffusion coefficient (ADC) maps. The program provides the functions for integrated registrations using mutual information, affine transform and non-rigid registration. The registration accuracy is evaluated by the calculation of the overlapped ratio of segmented bone regions and average distance difference of contours between reference and registered images. The performance of the developed software was tested using multimodal images of a patient who has the residual tumor of high grade gliomas. Registration accuracy of about 74% and average 2.3 mm distance difference were calculated by the evaluation method of bone segmentation and contour extraction. The registration accuracy can be improved as higher as 4% by the manual adjustment functions. Advanced MR images are analyzed using color maps for rCBV maps and quantitative calculation based on region of interest (ROI) for ADC maps. Then, multi-parameters on the same voxels are plotted on plane and constitute the multi-functional parametric maps of which x and y axis representing rCBV and ADC values. According to the distributions of functional parameters, tumor regions showing the higher vascularity and cellularity are categorized according to the criteria corresponding malignant gliomas. Determined volumes reflecting pathological and physiological characteristics of tumors are marked on anatomical images. By applying the multi-functional images, errors arising from using one type of image would be reduced and local regions representing higher probability as tumor cells would be determined for radiation treatment plan. Biological tumor characteristics can be expressed using image registration and multi-functional parametric maps in the developed software. The software can be considered to delineate clinical target volumes using advanced MR images with anatomical images.

• Korean Journal of Veterinary Research
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• v.35 no.2
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• pp.327-336
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• 1995

This study was carried out to develop the animal protein(feed for fry) that was isolated, purified and lyophilized the rumen ciliates from the healthy rumen contents which have $10^5-10^6/g$ ciliates and were discarded in abattoirs. The rumen ciliates are non-pathogenic, anaerobic and the weight of this protozoa is 2% of rumen content. The rumen protozoan and bacterial proteins both have a biological value for rats of 80-81, which is higher than the 72 of brewer's yeasts. Furthermore, the true digestibility and net protein utility of the protozoan protein are 91 and 73, much higher than those of bacterial(74 and 60) or yeast(84 and 60) proteins. The amino acids of rumen protozoa is nutritionally superior than the others. The size of rumen ciliates is $30-200{\times}20-110{\mu}m$ and so we had isolated and purified the rumen ciliates from the rumen contents by the physical methods. The purified rumen protozoa was lyophilized with freezing dryer. The results of this experiment were as follows : 1. Population dynamics of protozoan ciliates in slaughtered rumens; % of samples which small ciliates were predominated was 82.5%(52/63) and that of large ciliates was 17.5%(11/63). 1) predominant species of small ciliates were Entodinium ovinum and E nanellum. 2) predominant species of large ciliates were Epidinium ecaudatum and Diploplastron affine. 2. The lyophilized rumen ciliates which were isolated and purified from 1 kg of rumen content at the pH 6.2-6.8 was about 7.0 gram. 3. The nutrient analysis of lyophilized rqmen ciliates(LRC) was as follows: 1) Proximate analysis of the LRC and the composition of fry feed; moisture 8.05%(below 10.0), protein 35.37%(45), fat 5.39%(4.5), fiber 1.23%(below 2.5), ash 2.25%(below 15.0), Ca 0.26%(below 2.0), P 0.14%(below 1.1), energy 4,608.11(fish meal 5000 cal/g) 2) Amino acids (% in crude protein) of the LRC and the rotifer(Brachionus plicatilis); Arg 5.19%(4.50), His 2.50%(1.55), Ile 5.29%(3.45), Leu 8.11%(5.85), Lys 10.34%(6.15), Met 2.25% (0.85), Phe 5.66%(3.80), Thr 5.14% (3.45), Val 4.18%(3.90), Ala 4.13%(3.35), Asp 13.26%(8.25), Glu 16.62%(9.20), Gly 4.23%(3.10), Pro 3.25%(5.05), Ser 4.85%(3.85), Tyr 5.04%(3.05) 3) Fatty acids(% in fat) of the LRC and the rotifer(biological feed ; Brachionus plicatilis); myristic acid(C14:0) 3.27%(0.3), myristoleic acid(C14:1) 0.83%(-), palmitic acid(C16:0) 39.11% (23.5), palmitoleic acid(C16:1) 2.81%(2.0), stearic acid(C18:0) 9.36%(5.6), oleic acid(C18:1) 25.54%(3.5), linoleic acid(C18:2) 15.05%(32.9), linolenic acid(C18:3) 1.74%(9.8). Judging from the above investigated results, the analytical data of proximate analysis, amino acids, fatty acids of the purified and lyophilized rumen protozoa are reasonable for the feed of freshwater fishes(fry and fingerling). But it was disappointed of our expectation that the crude protein of lyophilized rumen ciliates contains low percentage, it was thought that because of the small ciliates(starch digester) in beef cattle rumens which were administered the concentrated feed, is much difficult to isolate and purify than the large ciliates(fiber digester).