• Journal of the Korean Society for information Management
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• v.25 no.4
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• pp.131-148
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• 2008

The purposes of the study are to design a key-frame extraction algorithm for constructing the virtual storyboard surrogates and to evaluate the efficiency of the proposed algorithm. To do this, first, the theoretical framework was built by conducting two tasks. One is to investigate the previous studies on relevance and image recognition and classification. Second is to conduct an experiment in order to identify their frames recognition pattern of 20 participants. As a result, the key-frame extraction algorithm was constructed. Then the efficiency of proposed algorithm(hybrid method) was evaluated by conducting an experiment using 42 participants. In the experiment, the proposed algorithm was compared to the random method where key-frames were extracted simply at an interval of few seconds(or minutes) in terms of accuracy in summarizing or indexing a video. Finally, ways to utilize the proposed algorithm in digital libraries and Internet environment were suggested.

• Smart Structures and Systems
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• v.33 no.6
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• pp.449-463
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• 2024

Automated crack detection is crucial for structural health monitoring and post-earthquake rapid damage detection. However, realizing high precision automatic crack detection in the absence of corresponding manual labeling presents a formidable challenge. This paper presents a novel crack segmentation transfer learning method and a novel crack segmentation model called Swin-CrackFormer. The proposed method facilitates efficient crack image style transfer through a meticulously designed data preprocessing technique, followed by the utilization of a GAN model for image style transfer. Moreover, the proposed Swin-CrackFormer combines the advantages of Transformer and convolution operations to achieve effective local and global feature extraction. To verify the effectiveness of the proposed method, this study validates the proposed method on three unlabeled crack datasets and evaluates the Swin-CrackFormer model on the METU dataset. Experimental results demonstrate that the crack transfer learning method significantly improves the crack segmentation performance on unlabeled crack datasets. Moreover, the Swin-CrackFormer model achieved the best detection result on the METU dataset, surpassing existing crack segmentation models.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.5
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• pp.2393-2398
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• 2012

Objectives: The purpose of this study was to evaluate the geometric accuracy of thoracic anatomic landmarks as target surrogates of intrapulmonary tumors for manual rigid registration during image-guided radiotherapy (IGRT). Methods: Kilovolt cone-beam computed tomography (CBCT) images acquired during IGRT for 29 lung cancer patients with 33 tumors, including 16 central and 17 peripheral lesions, were analyzed. We selected the "vertebrae", "carina", and "large bronchi" as the candidate surrogates for central targets, and the "vertebrae", "carina", and "ribs" as the candidate surrogates for peripheral lesions. Three to six pairs of small identifiable markers were noted in the tumors for the planning CT and Day 1 CBCT. The accuracy of the candidate surrogates was evaluated by comparing the distances of the corresponding markers after manual rigid matching based on the "tumor" and a particular surrogate. Differences between the surrogates were assessed using 1-way analysis of variance and post hoc least-significant-difference tests. Results: For central targets, the residual errors increased in the following ascending order: "tumor", "bronchi", "carina", and "vertebrae"; there was a significant difference between "tumor" and "vertebrae" (p = 0.010). For peripheral diseases, the residual errors increased in the following ascending order: "tumor", "rib", "vertebrae", and "carina"; There was a significant difference between "tumor" and "carina" (p = 0.005). Conclusions: The "bronchi" and "carina" are the optimal surrogates for central lung targets, while "rib" and "vertebrae" are the optimal surrogates for peripheral lung targets for manual matching of online and planned tumors.

• Current Optics and Photonics
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• v.8 no.4
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• pp.391-398
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• 2024

An image reconstruction algorithm is vital for the image quality of a photonic integrated interferometric imaging (PIII) system. However, image reconstruction algorithms have limitations that always lead to degraded image reconstruction. In this paper, a novel image reconstruction algorithm based on deep learning is proposed. Firstly, the principle of optical signal transmission through the PIII system is investigated. A dataset suitable for image reconstruction of the PIII system is constructed. Key aspects such as model and loss functions are compared and constructed to solve the problem of image blurring and noise influence. By comparing it with other algorithms, the proposed algorithm is verified to have good reconstruction results not only qualitatively but also quantitatively.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.810-813
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• 2004

Indexing and Retrieving images from large and varied collections using image content as a key is a challenging and important problem in computer vision application. In this paper, a color Content-based Image Retrieval (CBIR) system using hierarchical Region of Interest (ROI) query and indexing is presented. During indexing process, First, The ROIs on every image in the image database are extracted using a region-based image segmentation technique, The JSEG approach is selected to handle this problem in order to create color-texture regions. Then, Color features in form of histogram and correlogram are then extracted from each segmented regions. Finally, The features are stored in the database as the key to retrieve the relevant images. As in the retrieval system, users are allowed to select ROI directly over the sample or user's submission image and the query process then focuses on the content of the selected ROI in order to find those images containing similar regions from the database. The hierarchical region-of-interest query is performed to retrieve the similar images. Two-level search is exploited in this paper. In the first level, the most important regions, usually the large regions at the center of user's query, are used to retrieve images having similar regions using static search. This ensures that we can retrieve all the images having the most important regions. In the second level, all the remaining regions in user's query are used to search from all the retrieved images obtained from the first level. The experimental results using the indexing technique show good retrieval performance over a variety of image collections, also great reduction in the amount of searching time.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.1
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• pp.19-25
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• 2024

This paper proposes an artificial intelligence-based personal information area object detection optimization method in an embedded system to de-identify personal information in video data. As an object detection optimization method, first, in order to increase the detection rate for personal information areas when detecting objects, a gyro sensor is used to collect the shooting angle of the image data when acquiring the image, and the image data is converted into a horizontal image through the collected shooting angle. Based on this, each learning model was created according to changes in the size of the image resolution of the learning data and changes in the learning method of the learning engine, and the effectiveness of the optimal learning model was selected and evaluated through an experimental method. As a de-identification method, a shuffling-based masking method was used, and double-key-based encryption of the masking information was used to prevent restoration by others. In order to reuse the original image, the original image could be restored through a security key. Through this, we were able to secure security for high personal information areas and improve usability through original image restoration. The research results of this paper are expected to contribute to industrial use of data without personal information leakage and to reducing the cost of personal information protection in industrial fields using video through de-identification of personal information areas included in video data.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.11
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• pp.878-883
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• 2020

Cryptographic algorithms are used to prevent the illegal manipulation of data. They are divided into public-key cryptosystems and symmetric-key cryptosystems. Public-key cryptosystems require considerable time for encryption and decryption compared to symmetric-key cryptosystem. On the other hand, key management, and delivery are easier for public-key cryptosystems than symmetric-key cryptosystems because different keys are used for encryption and decryption. Furthermore, hash functions are being used very effectively to verify the integrity of the digital content, as they always generate output with a fixed size using the data of various sizes as input. This paper proposes a method using RSA public-key cryptography and a hash function to determine if a digital image is deformed or not and to detect the manipulated location. In the proposed method, the entire image is divided into several blocks, 64×64 in size. The watermark is then allocated to each block to verify the deformation of the data. When deformation occurs, the manipulated pixel will be divided into smaller 4×4 sub-blocks, and each block will have a watermark to detect the location. The safety of the proposed method depends on the security of the cryptographic algorithm and the hash function.

• International Journal of Internet, Broadcasting and Communication
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• v.7 no.2
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• pp.56-64
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• 2015

The personal image information compiled through closed-circuit television (CCTV) will be open to the internet with the technology such as Long-Tail, Mash-Up, Collective Intelligence, Tagging, Open Application Programming Interface (Open-API), Syndication, Podcasting and Asynchronous JavaScript and XML (AJAX). The movie User Created Contents (UCC) connected to the internet with the skill of web 2.0 has the effects of abuse and threat without precedent. The purpose of this research is to develop the institutional and technological method to reduce these effects. As a result of this research, in terms of technology this paper suggests Privacy Zone Masking, IP Filtering, Intrusion-detection System (IDS), Secure Sockets Layer (SSL), public key infrastructure (PKI), Hash and PDF Socket. While in terms of management this paper suggests Privacy Commons and Privacy Zone. Based on CCTV-UCC linked to the above network, the research regarding personal image information security is expected to aid in realizing insight and practical personal image information as a specific device in the following research.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.1
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• pp.28-33
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• 2003

This paper proposes a database index and retrieval method using the PCA(Principal Component Analysis). We perform a scene change detection and key frame extraction from the DC Image constructed by DCT DC coefficients in the compressed video stream that is video compression standard such as MPEG. In the extracted key frame, we use the PCA, then we can make codebook that has a statistical data as a codeword, which is saved as a database index. We also provide retrieval image that are similar to user's query image in a video database. As a result of experiments, we confirmed that the proposed method clearly showed superior performance in video retrieval and reduced computation time and memory space.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.4
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• pp.1246-1262
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• 2021

Recent studies have demonstrated the strong ability of deep convolutional neural networks (CNNs) to significantly boost the performance in single image super-resolution (SISR). The key concern is how to efficiently recover and utilize diverse information frequencies across multiple network layers, which is crucial to satisfying super-resolution image reconstructions. Hence, previous work made great efforts to potently incorporate hierarchical frequencies through various sophisticated architectures. Nevertheless, economical SISR also requires a capable structure design to balance between restoration accuracy and computational complexity, which is still a challenge for existing techniques. In this paper, we tackle this problem by proposing a competent architecture called Enhanced U-Net Network (EUN), which can yield ready-to-use features in miscellaneous frequencies and combine them comprehensively. In particular, the proposed building block for EUN is enhanced from U-Net, which can extract abundant information via multiple skip concatenations. The network configuration allows the pipeline to propagate information from lower layers to higher ones. Meanwhile, the block itself is committed to growing quite deep in layers, which empowers different types of information to spring from a single block. Furthermore, due to its strong advantage in distilling effective information, promising results are guaranteed with comparatively fewer filters. Comprehensive experiments manifest our model can achieve favorable performance over that of state-of-the-art methods, especially in terms of computational efficiency.