• Computers and Concrete
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• 제14권3호
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• pp.315-325
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• 2014

Concrete is an important material in most of civil constructions. Many properties of concrete can be determined through analysis of concrete images. Image segmentation is the first step for the most of these analyses. An automated system for segmentation of concrete images into microstructures using texture analysis is proposed. The performance of five different classifiers has been evaluated and the results show that using an Artificial Neural Network classifier is the best choice for an automatic image segmentation of concrete.

• 한국멀티미디어학회논문지
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• 제9권12호
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• pp.1580-1587
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• 2006

The term molecular imaging can be broadly defined as the in vivo characterization and measurement of biologic processes at the cellular and molecular level. Optical imaging that has highly reproducibility and repetition used in molecular imaging research. In the bioluminescence imaging, animals carrying the luciferase gene are imaged with a cooled CCD(Charge-Coupled Device) camera to pick up the small number of photons transmitted through tissues. Molecular imaging analysis will allow us to observe the incipience and progression of the disease. But hardware device for molecular imaging and software for molecular image analysis were dependent on imports. In this paper, we suggest image processing methods and designed software for bioluminescence signal analysis. And we demonstrated high correlation(r=0.99) between our software's photon counts and commercial software's photon counts. ROI function and processing functions were accomplished without error. This study have the importance of the development software for bioluminescence image processing and analysis. And this study built the foundations for creative development of analysis methods. We expected this study lead the development of image technology.

• International Journal of Computer Science & Network Security
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• 제23권10호
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• pp.129-134
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• 2023

The greatest challenge of this century is the protection of stored and transmitted data over the network. This paper provides a new hybrid algorithm designed based on combination algorithms, in the proposed algorithm combined with Hill and the Advanced Encryption Standard Algorithms, to increase the efficiency of color image encryption and increase the sensitivity of the key to protect the RGB image from Keyes attackers. The proposed algorithm has proven its efficiency in encryption of color images with high security and countering attacks. The strength and efficiency of combination the Hill Chipper and Advanced Encryption Standard Algorithms tested by statical analysis for RGB images histogram and correlation of RGB images before and after encryption using hill cipher and proposed algorithm and also analysis of the secret key and key space to protect the RGB image from Brute force attack. The result of combining Hill and Advanced Encryption Standard Algorithm achieved the ability to cope statistically

• 한국측량학회:학술대회논문집
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• 한국측량학회 2003년도 추계학술발표회 논문집
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• pp.157-162
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• 2003

The 3D Image which embodies real object to 3D space of computer enables various geometrical analysis as well as visualization of complex 3D shape by giving sense for the real and cubic effect that can not be offered in 2D image. Human gives real object to same physical properties in 3D space imagination world of computer, and it is expected that this enables offering of various information by user strengthening interface between human-computer to observe object in real condition. In this study, formal style routine of 3D image creation applying digital photogrammetry was designed for more practical, highly trusty 3D image creation, and the system was emboded using object-oriented technique which strengthen user interface. Also, the discontinuity information about rock slope using 3D image is acquired that is orientation, persistence, spacing and aperture etc.

• 반도체디스플레이기술학회지
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• 제12권3호
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• pp.59-64
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• 2013

Accurate and robust image registration is important task in many applications such as image retrieval and computer vision. To perform the image registration, essential required steps are needed in the process: feature detection, extraction, matching, and reconstruction of image. In the process of these function, feature extraction not only plays a key role, but also have a big effect on its performance. There are two representative algorithms for extracting image features, which are scale invariant feature transform (SIFT) and speeded up robust feature (SURF). In this paper, we present and evaluate two methods, focusing on comparative analysis of the performance. Experiments for accurate and robust feature detection are shown on various environments such like scale changes, rotation and affine transformation. Experimental trials revealed that SURF algorithm exhibited a significant result in both extracting feature points and matching time, compared to SIFT method.

• International Journal of Contents
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• 제4권1호
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• pp.12-15
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• 2008

In this paper, we propose a new method of background removing for digital self-adaptive acquisition in medical X-ray imaging. We analysis the construction of video digital acquisition system and main factors of acquired image quality, propose a more efficiency method to against background non-uniformly. With proposed method, non-uniform illumination back ground was well removed without image quality degradation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권4호
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• pp.1714-1729
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• 2018

Change detection of remote sensing images is a profound challenge in the field of remote sensing image analysis. This paper proposes a novel change detection method for bitemporal remote sensing images based on feature fusion and fuzzy c-means (FCM). Different from the state-of-the-art methods that mainly utilize a single image feature for difference image construction, the proposed method investigates the fusion of multiple image features for the task. The subsequent problem is regarded as the difference image classification problem, where a modified fuzzy c-means approach is proposed to analyze the difference image. The proposed method has been validated on real bitemporal remote sensing data sets. Experimental results confirmed the effectiveness of the proposed method.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 ITC-CSCC -2
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• pp.847-850
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• 2000

This paper proposes an image compressing and template matching algorithm for face image using GHA (Generalized Hebbian Algorithm). GHA is a part of PCA (Principal Component Analysis), that has single-layer perceptrons and operates and self-organizing performance. We used this algorithm for feature extraction of face shape, and our simulations verify the high performance for the proposed method. The shape for face in the fact that the eigenvector of face image can be efficiently represented as a coefficient that can be acquired by a set of basis is to compress data of image. From the simulation results, the mean PSNR performance is 24.08[dB] at 0.047bpp, and reconstruction experiment shows that good reconstruction capacity for an image that not joins at leaning.

• 한국컴퓨터정보학회논문지
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• 제7권4호
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• pp.121-126
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• 2002

3차원 영상은 2차원 영상과 달리 사물을 직접 볼 때처럼 입체감을 느낄 수 있으며 영상을 통해 바라보는 공간과 자신의 공간 연결이 더 자연스러워지므로 시각정보에 큰 영향을 주고 있다. 2차원 영상을 3차원으로 변환하기 위하여 몇 가지 방법이 제시되고 있다. 본 논문에서는 3차원 영상을 변환하기 위하여 2차원 단일 영상을 사용하지 알고 계속적으로 입력되는 다중 영상을 MPEG의 움직임 벡터를 적용한 공간적 분석 알고리즘을 제안한 결과 실험대상으로부터 3차원 효과를 확인하였다.

• 한국산업융합학회 논문집
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• 제27권3호
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• pp.563-572
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• 2024

Image processing and computer vision technologies are becoming increasingly important in a variety of application fields that require techniques and tools for sophisticated image analysis. In particular, image segmentation is a technology that plays an important role in image analysis. In this study, in order to identify recent research trends on image segmentation techniques, we used the Web of Science(WoS) database to analyze the R&D topography based on the network structure of the author's keyword co-occurrence matrix. As a result, from 2015 to 2023, as a result of the analysis of the R&D map of research articles on image segmentation, R&D in this field is largely focused on four areas of research and development: (1) researches on collecting and preprocessing image data to build higher-performance image segmentation models, (2) the researches on image segmentation using statistics-based models or machine learning algorithms, (3) the researches on image segmentation for medical image analysis, and (4) deep learning-based image segmentation-related R&D. The scientometrics-based analysis performed in this study can not only map the trajectory of R&D related to image segmentation, but can also serve as a marker for future exploration in this dynamic field.