• 센서학회지
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• 제6권5호
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• pp.376-384
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• 1997

본 논문에서는 에지 영역을 보상한 원격 센싱된 인공위성 화상의 부호화 기법을 제안하였다. 이 기법에서는 인공위성 화상데이타의 분광적 반사 특성에 따라 각 화소벡터를 분류한 후, 각 분류영역에 대하여 대역내 및 대역간 중복성을 각간 제거하기 위하여 분류영역별 대역내 벡터양자화 및 분류영역별 대역간 예측을 행한다. 에지영역의 경우에 주변블럭의 영역정보 및 양자화된 기준대역의 화소값을 이용하여 에지영역을 보상한다. 그후, 대역간 중복성을 효과적으로 제거하기 위하여 보상된 영역정보를 이용하여 분류영역별 대역간 예측을 행한다. 실제 LANDSAT-TM 인공위성 화상데이타에 대한 실험을 통하여 제안한 기법의 부호화 효율이 기존의 기법에 비하여 우수함을 확인하였다.

• 과학교육연구지
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• 제34권1호
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• pp.33-46
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• 2010

본 연구는 예비 과학 교사들이 보유한 선호 기피하는 과학 교사에 대한 이미지를 알아보는데 목적이 있다. 대구시 소재 경북대학교에 재학 중인 예비 교사 316명을 대상으로 중 고등학교에서 경험한 가장 기억에 남는 선호 기피하는 과학 교사에 대한 이미지에 대해 자유로운 형식으로 서술하도록 하였다. 그 결과 과학 수업 상황, 과학 교사의 이미지의 2가지 영역으로 범주화할 수 있었으며 각각은 다시 세부 영역으로 나누어졌다. 먼저, 과학 수업 상황에서 선호하는 수업 형태는 실험, 관찰, 체험, 관측이었으며 기피하는 것은 문답법이었다. 수업자료로는 프린트, 학습지, 보고서 등의 인쇄 매체를 선호하며 판서자료(칠판)를 기피하는 것으로 드러났다. 그리고 교과지도 방법으로는 이론, 개념 중심으로 지도하는 것을 선호하며 일방적인 암기나 문제 풀이는 기피하였다. 교과 내용 설명방식으로는 상세하게 체계적으로 설명하는 것을 선호하는 반면 설명 부족, 학생을 이해시키지 못하는 방식을 기피하였다. 또한 화기애애, 자유롭게 공부할 수 있는 수업 분위기가 되기를 바라는 예비 과학 교사들이 많았고 딱딱, 지루, 조용한 학습 분위기를 기피하는 경향을 보였다. 둘째, 과학 교사의 이미지에서는 교사의 자질로 인격과 의견을 존중할 줄 아는 자상하고 배려심 깊은 선생님을 선호하고 있었고 반대로 학생에게 신경 쓰지 않고 학생을 인격적으로 무시하는 선생님을 가장 싫어하였다. 교사의 특성 면에서는 맑고 씩씩한 목소리와 밝은 표정, 온화한 표정을 선호하고 튀는 외모와 딱딱한 스타일을 싫어하였다. 이 연구결과에 기초하여 교사의 이미지에 대한 연구가 실증적으로 이루어질 필요가 있으며 선호하거나 기피하는 교사의 이미지는 교육 행정가, 학생, 학부모, 교사 등에 따라 달라질 수 있으므로 이들의 의견을 종합하여 비교하는 연구가 이루어져야 한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권2호
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• pp.391-411
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• 2023

Aggregates play the skeleton and supporting role in the construction field, high-precision measurement and high-efficiency analysis of aggregates are frequently employed to evaluate the project quality. Aiming at the unbalanced operation time and segmentation accuracy for multi-class segmentation algorithms of aggregate images, a Chaotic Sparrow Search Algorithm (CSSA) is put forward to optimize it. In this algorithm, the chaotic map is combined with the sinusoidal dynamic weight and the elite mutation strategies; and it is firstly proposed to promote the SSA's optimization accuracy and stability without reducing the SSA's speed. The CSSA is utilized to optimize the popular multi-class segmentation algorithm-Multiple Entropy Thresholding (MET). By taking three METs as objective functions, i.e., Kapur Entropy, Minimum-cross Entropy and Renyi Entropy, the CSSA is implemented to quickly and automatically calculate the extreme value of the function and get the corresponding correct thresholds. The image adaptive multi-class segmentation model is called CSSA-MET. In order to comprehensively evaluate it, a new parameter I based on the segmentation accuracy and processing speed is constructed. The results reveal that the CSSA outperforms the other seven methods of optimization performance, as well as the quality evaluation of aggregate images segmented by the CSSA-MET, and the speed and accuracy are balanced. In particular, the highest I value can be obtained when the CSSA is applied to optimize the Renyi Entropy, which indicates that this combination is more suitable for segmenting the aggregate images.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권11호
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• pp.4153-4169
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• 2014

To recognize F5-like (such as F5 and nsF5) steganographic algorithm from multi-class stego images, a recognition algorithm based on the identifiable statistical feature (IDSF) of F5-like steganography is proposed in this paper. First, this paper analyzes the special modification ways of F5-like steganography to image data, as well as the special changes of statistical properties of image data caused by the modifications. And then, by constructing appropriate feature extraction sources, the IDSF of F5-like steganography distinguished from others is extracted. Lastly, based on the extracted IDSFs and combined with the training of SVM (Support Vector Machine) classifier, a recognition algorithm is presented to recognize F5-like stego images from images set consisting of a large number of multi-class stego images. A series of experimental results based on the detection of five types of typical JPEG steganography (namely F5, nsF5, JSteg, Steghide and Outguess) indicate that, the proposed algorithm can distinguish F5-like stego images reliably from multi-class stego images generated by the steganography mentioned above. Furthermore, even if the types of some detected stego images are unknown, the proposed algorithm can still recognize F5-like stego images correctly with high accuracy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권10호
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• pp.5015-5038
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• 2018

In this paper, a novel spectral-spatial joint sparse representation algorithm for hyperspectral image classification is proposed based on multi-layer superpixels in various scales. Superpixels of various scales can provide complete yet redundant correlated information of the class attribute for test pixels. Therefore, we design a joint sparse model for a test pixel by sampling similar pixels from its corresponding superpixels combinations. Firstly, multi-layer superpixels are extracted on the false color image of the HSI data by principal components analysis model. Secondly, a group of discriminative sampling pixels are exploited as reconstruction matrix of test pixel which can be jointly represented by the structured dictionary and recovered sparse coefficients. Thirdly, the orthogonal matching pursuit strategy is employed for estimating sparse vector for the test pixel. In each iteration, the approximation can be computed from the dictionary and corresponding sparse vector. Finally, the class label of test pixel can be directly determined with minimum reconstruction error between the reconstruction matrix and its approximation. The advantages of this algorithm lie in the development of complete neighborhood and homogeneous pixels to share a common sparsity pattern, and it is able to achieve more flexible joint sparse coding of spectral-spatial information. Experimental results on three real hyperspectral datasets show that the proposed joint sparse model can achieve better performance than a series of excellent sparse classification methods and superpixels-based classification methods.

• 대한방사선기술학회지:방사선기술과학
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• 제45권1호
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• pp.41-47
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• 2022

The purpose of this study is to apply a deep learning model that can distinguish lung perfusion and lung ventilation images in nuclear medicine, and to evaluate the image classification ability. Image data pre-processing was performed in the following order: image matrix size adjustment, min-max normalization, image center position adjustment, train/validation/test data set classification, and data augmentation. The convolutional neural network(CNN) structures of VGG-16, ResNet-18, Inception-ResNet-v2, and SE-ResNeXt-101 were used. For classification model evaluation, performance evaluation index of classification model, class activation map(CAM), and statistical image evaluation method were applied. As for the performance evaluation index of the classification model, SE-ResNeXt-101 and Inception-ResNet-v2 showed the highest performance with the same results. As a result of CAM, cardiac and right lung regions were highly activated in lung perfusion, and upper lung and neck regions were highly activated in lung ventilation. Statistical image evaluation showed a meaningful difference between SE-ResNeXt-101 and Inception-ResNet-v2. As a result of the study, the applicability of the CNN model for lung scintigraphy classification was confirmed. In the future, it is expected that it will be used as basic data for research on new artificial intelligence models and will help stable image management in clinical practice.

• 천문학회지
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• 제52권6호
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• pp.217-225
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• 2019

We apply a modified Convolutional Neural Network (CNN) model in conjunction with transfer learning to predict whether an active region (AR) would produce a ≥C-class or ≥M-class flare within the next 24 hours. We collect line-of-sight magnetogram samples of ARs provided by the SHARP from May 2010 to September 2018, which is a new data product from the HMI onboard the SDO. Based on these AR samples, we adopt the approach of shuffle-and-split cross-validation (CV) to build a database that includes 10 separate data sets. Each of the 10 data sets is segregated by NOAA AR number into a training and a testing data set. After training, validating, and testing our model, we compare the results with previous studies using predictive performance metrics, with a focus on the true skill statistic (TSS). The main results from this study are summarized as follows. First, to the best of our knowledge, this is the first time that the CNN model with transfer learning is used in solar physics to make binary class predictions for both ≥C-class and ≥M-class flares, without manually engineered features extracted from the observational data. Second, our model achieves relatively high scores of TSS = 0.640±0.075 and TSS = 0.526±0.052 for ≥M-class prediction and ≥C-class prediction, respectively, which is comparable to that of previous models. Third, our model also obtains quite good scores in five other metrics for both ≥C-class and ≥M-class flare prediction. Our results demonstrate that our modified CNN model with transfer learning is an effective method for flare forecasting with reasonable prediction performance.

• International Journal of Computer Science & Network Security
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• 제24권5호
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• pp.129-134
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• 2024

Object Detection is an emerging technology in the field of Computer Vision and Image Processing that deals with detecting objects of a particular class in digital images. It has considered being one of the complicated and challenging tasks in computer vision. Earlier several machine learning-based approaches like SIFT (Scale-invariant feature transform) and HOG (Histogram of oriented gradients) are widely used to classify objects in an image. These approaches use the Support vector machine for classification. The biggest challenges with these approaches are that they are computationally intensive for use in real-time applications, and these methods do not work well with massive datasets. To overcome these challenges, we implemented a Deep Learning based approach Convolutional Neural Network (CNN) in this paper. The Proposed approach provides accurate results in detecting objects in an image by the area of object highlighted in a Bounding Box along with its accuracy.

• 디지털융복합연구
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• 제11권8호
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• pp.273-279
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• 2013

본 연구에서는 변조전달함수(Modulation transfer function, MTF)를 이용한 컴퓨터 방사선영상의 감도 노출 분류에 따른 공간분해능을 평가하여 컴퓨터 방사선영상 획득의 기초자료로 제시하고자 하였다. 본 실험에서는 $100{\mu}mm$ 픽셀의 영상판을 이용하여 엣지법의 MTF를 측정하였다. 방사선 선질은 IEC 61267에서 권고하고 있는 RQA5를 이용하였다. X-선이 조사된 영상판은 감도 노출 분류를 50, 100, 200, 300, 400, 600, 800, 1200으로 각각 설정한 후 디지털 영상화하였다. 최종 획득된 영상들은 공인된 영상분석 프로그램인 image J와 Origin 8.0을 이용하여 MTF 50%와 10%를 구하여 평가하였다. 그 결과 감도 노출 분류 200에서 가장 우수한 MTF 50%($1.979{\pm}0.114lp/mm$)와 MTF 10%($3.932{\pm}0.041$)의 공간주파수를 구하였다. 따라서 골절 등과 같이 높은 공간분해능을 요구하는 질병진단에 유용할 것으로 사료된다.

• 한국초등과학교육학회지:초등과학교육
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• 제28권3호
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• pp.245-262
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• 2009

This research was to examine the understandings of elementary school teachers on the phenomena related to light and image, and to survey their perception change related to learning contents of optics. The subjects were selected from the elementary teachers who were enrolled in a graduate course, 'Science education seminar' at an education college located in Chungchungbuk-Do, South Korea. Among the five students who exposed their perceptions clearly in the class, the three of them were selected who agreed to the proposal of the case study. To achieve the purpose of this study, semi-structured interviews following the conception test with the 3 elementary teachers were conducted. During the analysis of the data, additional interviews by phone, e-mail, and internet messenger were conducted if necessary. According to the results, all of the elementary school teachers lacked the scientific conceptions of the phenomena related to light and image. Unfortunately, their learning experiences did not help them to understand the scientific concepts. During the interviews, the teachers recognized the importance of the viewpoints of seeing, image, cognition of light, point light source to understand the phenomena related to light and image.