• Journal of the Korea Society of Computer and Information
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• v.24 no.5
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• pp.35-40
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• 2019

In this paper, we design and implement a partial denoising boundary matching system using indexing techniques. Converting boundary images to time-series makes it feasible to perform a fast search using indexes even on a very large image database. Thus, using this converting method we develop a client-server system based on the previous partial denoising research in the GUI(graphical user interface) environment. The client first converts a query image given by a user to a time-series and sends denoising parameters and the tolerance with this time-series to the server. The server identifies similar images from the index by evaluating a range query, which is constructed using inputs given from the client and sends the resulting images to the client. Experimental results show that our system provides many intuitive and accurate matching results.

• Clinical Endoscopy
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• v.55 no.5
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• pp.594-604
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• 2022

Over the past decade, technological advances in deep learning have led to the introduction of artificial intelligence (AI) in medical imaging. The most commonly used structure in image recognition is the convolutional neural network, which mimics the action of the human visual cortex. The applications of AI in gastrointestinal endoscopy are diverse. Computer-aided diagnosis has achieved remarkable outcomes with recent improvements in machine-learning techniques and advances in computer performance. Despite some hurdles, the implementation of AI-assisted clinical practice is expected to aid endoscopists in real-time decision-making. In this summary, we reviewed state-of-the-art AI in the field of gastrointestinal endoscopy and offered a practical guide for building a learning image dataset for algorithm development.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.16 no.4
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• pp.91-103
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• 2006

This paper proposes a methodology to evaluate the quality and level of difficulty of fingerprint image databases, which leads to objective evaluation for the performance of fingerprint recognition system. Influencing factors to fingerprint matching are defined and the matching performance between two fingerprint images is evaluated using segmentation and orientation filed. In this study, a hierarchical processing method is proposed to measure an orientation field, which is able to improve the matching speed and accuracy. The results of experiments demonstrate that the defined influencing factors can describe the characteristics of fingerprint databases. Level of difficulty for fingerprint databases enables the performance of fingerprint recognition algorithms to be evaluated and compared even with different databases.

• Journal of Korea Multimedia Society
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• v.8 no.3
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• pp.355-361
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• 2005

This paper proposed a system collecting image information by agents in multi-threaded environment and then retrieving them with content based image retrieval. This system uses multi threads to retrieve web information effectively, then improves efficiency of CPU cycles to reduce latency time, which is the time requesting queries, executing communication processing 4hat the retrieval agents perform and filtering the retrieval results. Also, the agents for image retrieval use Java language, which is platform independent, to be suitable for distributed environment. Using JDBC to save the retrieved images, the agents are connected to database. The images themselves are stored in distributed agents' databases, and only the image indexes are stored in an index server so that the efficiency of storage and retrieval time can be improved.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.7
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• pp.589-598
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• 2002

The color measuring/matching expert system is implemented with a new color measuring method that combines intelligent algorithms with image processing techniques. Color measuring part of the proposed system preprocesses the scanned original color input images to eliminate their distorted components by means of the image histogram technique of image pixels, and then extracts RGB(Red, Green, Blue)data among color information from preprocessed color input images. If the extracted RGB color data does not exist on the matching recipe databases, we can measure the colors for the user who want to implement the model that can search the rules for the color mixing information, using the intelligent modeling techniques such as fuzzy inference system and adaptive neuro-fuzzy inference system. Color matching part can easily choose images close to the original color for the user by comparing information of preprocessed color real input images with data-based measuring recipe information of the expert, from the viewpoint of the delta Eformula used in practical process.

• The Transactions of the Korea Information Processing Society
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• v.4 no.12
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• pp.2931-2946
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• 1997

In multimedia information retrieval applications, content-based image retrieval is essential for retrieving relevant multimedia documents. The purpose of our paper is to provide effective representation and efficient retrieval of images when a pixel-level original image is automatically or manually transformaed into its iconic image containing meaningful graphic descriptions, called icon objects. For this, we first propose new spatial match representationschemes to describe spatial relationships between icon objects accurately by expressing them as rectangles, rather than as points. In order to accelerate image searching, we also design an efficient retrieval method using a two-dimensional signature file organization. Finally, we show from our experiment that the proposed representation schemes achieve better retrieval effectiveness than the 9-DLT (Direction Lower Triangular) scheme.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.1
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• pp.288-301
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• 2017

In this paper, we propose an optimized algorithm for texture classification by computing a completed modeling of the local binary pattern (CLBP) instead of the traditional LBP of a scalable block size in an image. First, we show that the CLBP descriptor is a better representative than LBP by extracting more information from an image. Second, the CLBP features of scalable block size of an image has an adaptive capability in representing both gross and detailed features of an image and thus it is suitable for image texture classification. This paper successfully implements a machine learning scheme by applying the CLBP features of a scalable size to the Support Vector Machine (SVM) classifier. The proposed scheme has been evaluated on Outex and CUReT databases, and the evaluation result shows that the proposed approach achieves an improved recognition rate compared to the previous research results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.1
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• pp.288-307
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• 2016

Image distortions are typically characterized by degradations of structures. Dictionaries learned from natural images can capture the underlying structures in images, which are important for image quality assessment (IQA). This paper presents a general-purpose no-reference image quality metric using a GRadient-Induced Dictionary (GRID). A dictionary is first constructed based on gradients of natural images using K-means clustering. Then image features are extracted using the dictionary based on Euclidean-norm coding and max-pooling. A distortion classification model and several distortion-specific quality regression models are trained using the support vector machine (SVM) by combining image features with distortion types and subjective scores, respectively. To evaluate the quality of a test image, the distortion classification model is used to determine the probabilities that the image belongs to different kinds of distortions, while the regression models are used to predict the corresponding distortion-specific quality scores. Finally, an overall quality score is computed as the probability-weighted distortion-specific quality scores. The proposed metric can evaluate image quality accurately and efficiently using a small dictionary. The performance of the proposed method is verified on public image quality databases. Experimental results demonstrate that the proposed metric can generate quality scores highly consistent with human perception, and it outperforms the state-of-the-arts.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.10
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• pp.4060-4079
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• 2020

Blur is an important type of image distortion. How to evaluate the quality of blurred image accurately and efficiently is a research hotspot in the field of image processing in recent years. Inspired by the multi-scale perceptual characteristics of the human visual system (HVS), this paper presents a no-reference image blur/sharpness assessment method based on multi-scale local features in the spatial domain. First, considering various content has different sensitivity to blur distortion, the image is divided into smooth, edge, and texture regions in blocks. Then, the Gaussian scale space of the image is constructed, and the categorized contrast features between the original image and the Gaussian scale space images are calculated to express the blur degree of different image contents. To simulate the impact of viewing distance on blur distortion, the distribution characteristics of local maximum gradient of multi-resolution images were also calculated in the spatial domain. Finally, the image blur assessment model is obtained by fusing all features and learning the mapping from features to quality scores by support vector regression (SVR). Performance of the proposed method is evaluated on four synthetically blurred databases and one real blurred database. The experimental results demonstrate that our method can produce quality scores more consistent with subjective evaluations than other methods, especially for real burred images.

• Proceedings of the Korean Information Science Society Conference
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• 1999.10a
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• pp.299-301
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• 1999

최근 데이터베이스 시스템 분야에서는 각종 비쥬얼 시뮬레이터, 가상현실, 게임 등과 같은 응용이 등장함에 따라서 3차원 이미지 데이터의 중요성이 높아지게 되었고 이에 대한 검색 및 관리가 필요하게 되었다. 그래서 본 논문에서는 3차원 이미지 데이터베이스에서 내용기반 검색을 지원하기 위한 모델링 방법과 3차원 이미지 데이터베이스 시스템의 구조를 제시한다. 이를 위한 요소기술로서 3차원 객체의 모델링 기법과 객체간이 공간관계 표현 기법이 제시되었다.