• Journal of Animal Science and Technology
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• 제65권3호
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• pp.638-651
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• 2023

The objective of this study was to quantitatively estimate the level of grazing area damage in outdoor free-range pig production using a Unmanned Aerial Vehicles (UAV) with an RGB image sensor. Ten corn field images were captured by a UAV over approximately two weeks, during which gestating sows were allowed to graze freely on the corn field measuring 100 × 50 m². The images were corrected to a bird's-eye view, and then divided into 32 segments and sequentially inputted into the YOLOv4 detector to detect the corn images according to their condition. The 43 raw training images selected randomly out of 320 segmented images were flipped to create 86 images, and then these images were further augmented by rotating them in 5-degree increments to create a total of 6,192 images. The increased 6,192 images are further augmented by applying three random color transformations to each image, resulting in 24,768 datasets. The occupancy rate of corn in the field was estimated efficiently using You Only Look Once (YOLO). As of the first day of observation (day 2), it was evident that almost all the corn had disappeared by the ninth day. When grazing 20 sows in a 50 × 100 m² cornfield (250 m²/sow), it appears that the animals should be rotated to other grazing areas to protect the cover crop after at least five days. In agricultural technology, most of the research using machine and deep learning is related to the detection of fruits and pests, and research on other application fields is needed. In addition, large-scale image data collected by experts in the field are required as training data to apply deep learning. If the data required for deep learning is insufficient, a large number of data augmentation is required.

• 핵의학기술
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• 제17권2호
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• pp.72-77
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• 2013

Purpose: Whole-body PET using radiopharmaceutical is one of the imaging study methods for physiological changes of body. High specificity of the PET-CT examination is used to detect an early stages of cancer and metastatic cancer by imaging a physiological changes. During the imaging process, PET image has been characterized by a relatively low image quality due to its low sensitivity and the acquisition of random and scatter coincidences as well as patients figure. Therefore, the image quality as the changes of the acquisition times of patient weight was evaluated in this study. Materials and Methods: Thirty patients who presented to our hospital were enrolled. They were divided to normal, overweight, and obese group using BMI index, respectively. The patients with a liver disease and diabetes were excluded. $^{18}F-FDG$ was administered to the patients as 5.2 MBq per kg. After an hour from an injection, image acquisition was obtained as List mode in a part of liver in 1 bed. SNR (signal-to-noise ratio) of each groups acquisition times were confirmed from the calculated radiation counts and random fractions. The statistical significance of three groups was confirmed through one-way ANOVA test. On the basis of the counts of 2 minutes on normal group, the SNR of overweight group and obese group were compared. Results: The SNR were increased with loger aquisition time in 3 groups. In the condition of same acquisition time, the SNR had a statistical significance (P<0.05). The SNR were decreased to the normal, overweight, and obese, respectively. Liver activity had no significance difference on each group and RF had the significance differences (P<0.05). On the basis of the counts of 2 minutes on normal group, there were no statistical significance in a three minute acquisitions of overweight group and two minute acquisitions of obese group (P=0.150). Conclusion: In this study, the administrated amount of radiation dose did not adjust as the change of the patients weight. Increasing the acquisition time when the administration of the same amount of dose was able to get a good result of SNR. When the Based 2 minute on normal group, if overweight and obese case the increased acquisition time of 3 minute was able to obtain a similar SNR. On the basis of the normal group, the acquisition times of overweight and obese group were increased to 3 minutes per bed and the SNR were similar to the normal group.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2002년도 Proceedings of International Symposium on Remote Sensing
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• pp.40-44
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• 2002

Precision correction is the process of geometrically aligning images to a reference coordinate system using GCPs(Ground Control Points). Many applications of remote sensing data, such as change detection, mapping and environmental monitoring, rely on the accuracy of precision correction. However it is a very time consuming and laborious process. It requires GCP collection, the identification of image points and their corresponding reference coordinates. At typical satellite ground stations, GCP collection requires most of man-powers in processing satellite images. A method of automatic registration of satellite images is demanding. In this paper, we propose a new algorithm for automatic precision correction by GCP chips and RANSAC(Random Sample Consensus). The algorithm is divided into two major steps. The first one is the automated generation of ground control points. An automated stereo matching based on normalized cross correlation will be used. We have improved the accuracy of stereo matching by determining the size and shape of match windows according to incidence angle and scene orientation from ancillary data. The second one is the robust estimation of mapping function from control points. We used the RANSAC algorithm for this step and effectively removed the outliers of matching results. We carried out experiments with SPOT images over three test sites which were taken at different time and look-angle with each other. Left image was used to select UP chipsets and right image to match against GCP chipsets and perform automatic registration. In result, we could show that our approach of automated matching and robust estimation worked well for automated registration.

• Restorative Dentistry and Endodontics
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• 제48권2호
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• pp.16.1-16.11
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• 2023

Objectives: The aim of this study was to evaluate the influence of peak kilovoltage (kVp) and a metal artifact reduction (MAR) tool on image quality and the diagnosis of vertical root fracture (VRF) in cone-beam computed tomography (CBCT). Materials and Methods: Twenty single-rooted human teeth filled with an intracanal metal post were divided into 2 groups: control (n = 10) and VRF (n = 10). Each tooth was placed into the socket of a dry mandible, and CBCT scans were acquired using a Picasso Trio varying the kVp (70, 80, 90, or 99), and the use of MAR (with or without). The examinations were assessed by 5 examiners for the diagnosis of VRF using a 5-point scale. A subjective evaluation of the expression of artifacts was done by comparing random axial images of the studied protocols. The results of the diagnoses were analyzed using 2-way analysis of variance and the Tukey post hoc test, the subjective evaluations were compared using the Friedman test, and intra-examiner reproducibility was evaluated using the weighted kappa test (α = 5%). Results: The kVp and MAR did not influence the diagnosis of VRF (p > 0.05). According to the subjective classification, the 99 kVp protocol with MAR demonstrated the least expression of artifacts, while the 70 kVp protocol without MAR led to the most artifacts. Conclusions: Protocols with higher kVp combined with MAR improved the image quality of CBCT examinations. However, those factors did not lead to an improvement in the diagnosis of VRF.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권1호
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• pp.192-210
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• 2024

Most frequency-domain remote sensing image watermarking algorithms embed watermarks at random locations, which have negative impact on the watermark invisibility. In this study, we propose an adaptive watermarking scheme for remote sensing images that considers the information complexity to select where to embed watermarks to improve watermark invisibility without affecting algorithm robustness. The scheme converts remote sensing images from RGB to YCbCr color space, performs two-level DWT on luminance Y, and selects the high frequency coefficient of the low frequency component (HH^Y₂) as the watermark embedding domain. To achieve adaptive embedding, HH^Y₂ is divided into several 8*8 blocks, the entropy of each sub-block is calculated, and the block with the maximum entropy is chosen as the watermark embedding location. During embedding phase, the watermark image is also decomposed by two-level DWT, and the resulting high frequency coefficient (HH^W₂) is then embedded into the block with maximum entropy using α- blending. The experimental results show that the watermarked remote sensing images have high fidelity, indicating good invisibility. Under varying degrees of geometric, cropping, filtering, and noise attacks, the proposed watermarking can always extract high identifiable watermark images. Moreover, it is extremely stable and impervious to attack intensity interference.

• 지구물리와물리탐사
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• 제23권3호
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• pp.157-167
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• 2020

최근 인공지능 기술의 발전과 함께 물리탐사의 다양한 분야에서도 인공지능의 핵심 기술인 머신러닝의 활용도가 증가하고 있다. 또한 머신러닝 및 딥러닝을 활용한 연구는 이미지, 비디오, 음성, 자연어 등 다양한 태스크의 추론 정확도를 높이기 위해 복잡한 알고리즘들이 개발되고 있고, 더 나아가 자료의 특성, 알고리즘 구조 및 하이퍼 파라미터의 최적화를 위한 자동 머신러닝(AutoML) 분야로 그 폭을 넓혀가고 있다. 본 연구에서는 AutoML 분야 중에서도 하이퍼 파라미터(hyperparameter) 자동 탐색을 위한 베이지안 최적화 기술에 중점을 두었으며, 본 기술을 물리탐사 분야에서도 암상 분류(facies classification) 문제에 적용했다. Vincent field의 현장 물리검층 및 탄성파 자료를 이용하여 암상 및 공극유체를 분류하는 지도학습 기반 모델에 적용하였고, 랜덤 탐색 기법의 결과와 비교하여 베이지안 최적화 기반 예측 프레임워크의 효율성을 검증하였다.

• 한국식품조리과학회지
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• 제28권6호
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• pp.721-729
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• 2012

This paper aimed to investigate the influence of consumer lifestyle on consumer selection of bakery cafe attributes. Data were collected through a self-administered questionnaire by 403 random consumers between the ages 20s and 30s in several bakery cafes in Seoul and Gyonggi area. Different methods of statistical analysis had been used such as frequency analysis, factor analysis, k-means clustering analysis, cross tabulation, one way ANOVA and Duncan's multiple range test with SPSS for Window 13.0 package. First, when analyzing the 16 questions of comsumer lifestyles, four factors were extracted: 'dining out-oriented factor', 'achievement-oriented factor', 'brand-oriented factor', and 'health-oriented factor'. Second, the respondents were divided into three groups by k-means cluster analysis: no interest group, dining-out & value oriented group, and health-brand oriented group. Third, consumer's bakery cafe attributes were categorized into five factors including 'food', 'convenience and image', 'store promotion', 'positive dining experience', and 'menu & merchandises'. Finally when analyzing the differences in the selection of bakery cafe attributes according to consumer's lifestyles, it showed a significant differences.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2009년도 추계학술대회
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• pp.949-952
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• 2009

본 논문에서는 다층구조를 갖는 trellis 부호를 이용한 정보부호화 기반 워터마킹 방법에 대하여 연구하였다. 영상을 $8{\times}8$블록으로 중복되지 않게 나누어 DCT변환을 수행하고 각 블록으로부터 12개의 중간주파수 대역의 계수를 추출한다. 이를 다층구조를 갖는 trellis 부호화의 각 단계에서 평균이 0이고 분산이 1인 가우시안 난수와 비교하여 선형상관계수가 최소인 벡터를 Viterbi 알고리즘으로 구하고 이 벡터를 원 영상에 삽입하여 워터마킹된 영상을 얻는다. 제안 방법의 성능을 평가하기 위해 다수의 영상에 대한 평균 비트오차율을 계산하여 성능을 비교하였다.

• 한국정보통신학회논문지
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• 제13권12호
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• pp.2677-2684
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• 2009

본 논문에서는 trellis 부호 및 엔트로피 마스킹을 이용한 정보부호화 기반 워터마킹 방법에 대하여 연구하였다. 영상을 $8{\times}8$ 블록으로 중복되지 않게 나누어 DCT 변환을 수행하고 각 블록으로부터 16개의 중간주파수 대역의 계수를 추출한다. 이를 trellis 부호화의 각 단계에서 평균이 0이고 분산이 1인 가우시안 난수와 비교하여 선형상관계수 및 왓슨거리의 선형결합이 최소인 벡터를 Viterbi 알고리즘으로 구하고 이를 원 영상에 삽입하여 워터마킹된 영상을 얻는다. 영상의 특성을 고려하기 위해 삽입벡터를 구할 때 엔트로피 마스킹 함수를 사용하여 선형상관계수와 왓슨거리의 가중치를 다르게 적용한다. 제안방법의 성능을 평가하기 위해 다수의 영상에 대한 평균비트오차율을 계산하여 성능을 비교하였으며, 평균비트오차율 측면에서 성능 개선이 있었다.

• 한국통신학회논문지
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• 제29권5C호
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• pp.706-715
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• 2004

본 논문에서 제안되는 워터마크(Watermark) 삽입 알고리즘은 웨이블릿 변환 영역에서 구성되는 부대역간의 트리구조(Tree structure)와 공간 영역에서의 윤곽선 정보를 이용하여 워터마크를 삽입할 영역을 결정하고 삽입한다. 먼저 생성되는 고주파 성분의 부대역으로부터 저주파 부대역으로 중요 주파수 영역을 예측하게 되는데 웨이블릿 변환영역에서 구성된 트리구조에서 높은 주파수를 가지는 LHI 부대역을 4${\times}$4의 부행렬(Submatrix)로 나누고 행렬에 대한 평균과 이들에 의해 구성되는 블록 행렬(Block matrix)로부터 전체 평균 및 워터마크 삽입에 이용될 임계값을 얻는다. 또한 주파수 영역에서 구해진 에너지 특성에 대한 블록 행렬과 공간 영역에서 얻어진 영상의 윤곽선 정보에 의해 워터마크가 삽입될 위치인 키맵(Keymap)이 구해진다. 구해진 키맵에 따라서 LFSR(Linear feedback shift register)을 이용하여 발생된 무작위 순열(Random sequence)를 웨이블릿 도메인에서 이웃 웨이블릿 계수간의 관계를 이용하여 삽입한다. 최종적으로 역 웨이블릿 변환을 취함으로써 워터마크가 삽입된 영상을 생성한다. 제안된 워터마킹 알고리즘은 JPEG과 같은 압축과 Blurring, Sharpening, 그리고 가우시안(Gaussian) 잡음 등의 공격에 대해서도 기존의 방식에 비해 약 2㏈ 절도 높은 PSNR(Peak signal to noise ratio)를 보이면서 2%에서 8% 정도 높은 NR(Normalized correlation)를 가져서 좋은 특성을 나타냈다.