• Journal of Information Technology Services
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• v.18 no.2
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• pp.123-141
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• 2019

The goal of this study is to review the major research trend on the convergence studies of AI and healthcare technologies. For the study, 15,260 English articles on AI and healthcare related topics were collected from Scopus for 55 years from 1963, and text mining techniques were conducted. As a result, seven key research topics were defined : "AI for Clinical Decision Support System (CDSS)", "AI for Medical Image", "Internet of Healthcare Things (IoHT)", "Big Data Analytics in Healthcare", "Medical Robotics", "Blockchain in Healthcare", and "Evidence Based Medicine (EBM)". The result of this study can be utilized to set up and develop the appropriate healthcare R&D strategies for the researchers and government. In this study, text mining techniques such as Text Analysis, Frequency Analysis, Topic Modeling on LDA (Latent Dirichlet Allocation), Word Cloud, and Ego Network Analysis were conducted.

• Journal of the Korean Graphic Arts Communication Society
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• v.18 no.2
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• pp.55-68
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• 2000

Joint photographic experts group (JPEG) is a standard still-image compression technique, established by the international organization for standardization (ISO) and international telecommunication standardization sector (ITUT). The standard is intended to be utilized in the various kinds of color still imaging systems as a standard color image coding format. Because JPEG is a lossy compression, the decompressed image pixel values are not the same as the value before compression. Various distortions of JPEG compression and JPEG recompression has been reported in various papers. The Image compressed by JPEG is often recompressed by same type compression method in JPEG. In general, JPEG is a lossy compression and the quality of compressed image is predicted that is varied in according to recompression Q-factor. In this paper, four difference color samples(photo image, gradient image, gradient image, vector drawing image, text image) were compressed in according to various Q-factor, and then the compressed images were recompressed according to various Q-factor once again. As the result, this paper evaluate the variation of image quality and file size in JPEG recompression and recommed the optimum recompression factor.

• The KIPS Transactions:PartB
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• v.13B no.6 s.109
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• pp.591-600
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• 2006

This paper describes the development and implementation of a algorithm to decompose word images from image regions mixed text/graphics in document images using statistical analyses. To decompose word images from image regions, the character components need to be separated from graphic components. For this process, we propose a method to separate them with an analysis of box-plot using a statistics of structural components. An accuracy of this method is not sensitive to the changes of images because the criterion of separation is defined by the statistics of components. And then the character regions are determined by analyzing a local crowdedness of the separated character components. finally, we devide the character regions into text lines and word images using projection profile analysis, gap clustering, special symbol detection, etc. The proposed system could reduce the influence resulted from the changes of images because it uses the criterion based on the statistics of image regions. Also, we made an experiment with the proposed method in document image processing system for keyword spotting and showed the necessity of studying for the proposed method.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.1
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• pp.152-159
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• 2010

Existing character recognition algorithm recognize characters in simple conditional. It has the disadvantage that recognition rates often drop drastically when input document image has low quality, rotated text, various font or size text because of external noise or data loss. In this paper, proposes the optical character recognition algorithm which using bicubic interpolation method for the catalog retrieval when the input image has rotated text, blurred, various font and size. In this paper, applied optical character recognition algorithm consist of detection and recognition part. Detection part applied roberts and hausdorff distance algorithm for correct detection the catalog of book. Recognition part applied bicubic interpolation to interpolate data loss due to low quality, various font and size text. By the next time, applied rotation for the bicubic interpolation result image to slant proofreading. Experimental results show that proposal method can effectively improve recognition rate 6% and search-time 1.077s process result.

• Journal of Korea Multimedia Society
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• v.25 no.10
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• pp.1535-1545
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• 2022

Sign language has completely different meaning depending on the direction of the hand or the change of facial expression even with the same gesture. In this respect, it is crucial to capture the spatial-temporal structure information of each movement. However, sign language translation studies based on Sign2Gloss2Text only convey comprehensive spatial-temporal information about the entire sign language movement. Consequently, detailed information (facial expression, gestures, and etc.) of each movement that is important for sign language translation is not emphasized. Accordingly, in this paper, we propose Spatial-temporal Keypoints Centered Sign2Gloss2Text Translation, named STKC-Sign2 Gloss2Text, to supplement the sequential and semantic information of keypoints which are the core of recognizing and translating sign language. STKC-Sign2Gloss2Text consists of two steps, Spatial Keypoints Embedding, which extracts 121 major keypoints from each image, and Temporal Keypoints Embedding, which emphasizes sequential information using Bi-GRU for extracted keypoints of sign language. The proposed model outperformed all Bilingual Evaluation Understudy(BLEU) scores in Development(DEV) and Testing(TEST) than Sign2Gloss2Text as the baseline, and in particular, it proved the effectiveness of the proposed methodology by achieving 23.19, an improvement of 1.87 based on TEST BLEU-4.

• Journal of Korea Multimedia Society
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• v.10 no.7
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• pp.856-868
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• 2007

We propose an effective method for block classification in a document image. The gray level document image is converted to the binary image for a block segmentation. This binary image would be smoothed to find the locations and sizes of each block. And especially during this smoothing, the inner block heights of each block are obtained. The gray level image is divided to several blocks by these location informations. The SGLDM(spatial gray level dependence matrices) are made using the each gray-level document block and the seven second-order statistical texture features are extracted from the (0,1) direction's SGLDM which include the document attributes. Document image blocks are classified to two groups, text and non-text group, by the inner block height of the block at the nearest neighbor rule. The seven texture features(that were extracted from the SGLDM) are used for the five detail categories of small font, large font, table, graphic and photo blocks. These document blocks are available not only for structure analysis of document recognition but also the various applied area.

• Journal of Korea Society of Industrial Information Systems
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• v.8 no.2
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• pp.14-20
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• 2003

This paper presents a text extraction method of camera based document image. It is more difficult to recognize camera based document image in comparison with scanner based image because of segmentation problem due to variable lighting condition and versatile fonts. Both document binarization and character extraction are important processes to recognize camera based document image. After converting color image into grey level image, gray level normalization is used to extract character region independent of lighting condition and background image. Local adaptive binarization method is then used to extract character from the background after the removal of noise. In this character extraction step, the information of the horizontal and vertical projection and the connected components is used to extract character line, word region and character region. To evaluate the proposed method, we have experimented with documents mixed Hangul, English, symbols and digits of the ETRI database. An encouraging binarization and character extraction results have been obtained.

• IEIE Transactions on Smart Processing and Computing
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• v.5 no.5
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• pp.310-318
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• 2016

Emerging technologies such as the Internet of Things (IoT) and the Advanced Driver Assistant System (ADAS) often have image transmission functions with tough constraints, like low power and/or low delay, which require that they adopt line-based, low memory compression methods instead of existing frame-based image compression standards. Bit rate control in the conventional frame-based compression systems requires a lot of hardware resources when the scope of handled data falls at the frame level. On the other hand, attempts to reduce the heavy hardware resource requirement by focusing on line-level processing yield uneven image quality through the frame. In this paper, we propose a bit rate control that maintains consistency in image quality through the frame and improves the legibility of text regions. To find the line characteristics, the proposed bit rate control tests each line for ease of compression and the existence of text. Experiments on the proposed bit rate control show peak signal-to-noise ratios (PSNRs) similar to those of conventional bit rate controls, but with the use of significantly fewer hardware resources.

• Journal of the Korea Society of Computer and Information
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• v.16 no.4
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• pp.61-71
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• 2011

In this paper, we proposed the method for detecting text region on image using DCT-coefficient and transition-map analysis. The detecting rate of traditional method for detecting text region using DCT-coefficient analysis is high, but false positive detecting rate also is high and the method using transition-map often reject true text region in step of verification because of sticky threshold. To overcome these problems, we generated PTRmap(Promising Text Region map) through DCT-coefficient analysis and applied PTRmap to method for detecting text region using transition map. As the result, the false positive detecting rate decreased as compared with the method using DCT-coefficient analysis, and the detecting rate increased as compared with the method using transition map.

• Journal of Internet Computing and Services
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• v.23 no.5
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• pp.145-154
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• 2022

Automatic Target Recognition (ATR) technology is emerging as a core technology of Future Combat Systems (FCS). Conventional ATR is performed based on IMINT (image information) collected from the SAR sensor, and various image-based deep learning models are used. However, with the development of IT and sensing technology, even though data/information related to ATR is expanding to HUMINT (human information) and SIGINT (signal information), ATR still contains image oriented IMINT data only is being used. In complex and diversified battlefield situations, it is difficult to guarantee high-level ATR accuracy and generalization performance with image data alone. Therefore, we propose a knowledge graph-based ATR method that can utilize image and text data simultaneously in this paper. The main idea of the knowledge graph and deep model-based ATR method is to convert the ATR image and text into graphs according to the characteristics of each data, align it to the knowledge graph, and connect the heterogeneous ATR data through the knowledge graph. In order to convert the ATR image into a graph, an object-tag graph consisting of object tags as nodes is generated from the image by using the pre-trained image object recognition model and the vocabulary of the knowledge graph. On the other hand, the ATR text uses the pre-trained language model, TF-IDF, co-occurrence word graph, and the vocabulary of knowledge graph to generate a word graph composed of nodes with key vocabulary for the ATR. The generated two types of graphs are connected to the knowledge graph using the entity alignment model for improvement of the ATR performance from images and texts. To prove the superiority of the proposed method, 227 documents from web documents and 61,714 RDF triples from dbpedia were collected, and comparison experiments were performed on precision, recall, and f1-score in a perspective of the entity alignment..