• The Journal of Korean Institute of Communications and Information Sciences
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• v.23 no.6
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• pp.1546-1553
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• 1998

In this paper, we investigate a human identification scheme using visual cryptography, which can decode concealed images without and crytographic computations. T.Katoh & H.Imai proposed a secret sharing scheme which can conceal two query images into one displayed image for the identification. the generalized construction of the share matrix is proposed to conceal the multiple query images by stacking user slides o the displayed image. Furthermore, an extended methodis proposed in which group of slides can conceal an independent secret image as well as the pairs of slides can.

• Journal of Biomedical Engineering Research
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• v.43 no.6
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• pp.441-447
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• 2022

In this paper, we propose a pill identification model using engraved text feature and image feature such as shape and color, and compare it with an identification model that does not use engraved text feature to verify the possibility of improving identification performance by improving recognition rate of the engraved text. The data consisted of 100 classes and used 10 images per class. The engraved text feature was acquired through Keras OCR based on deep learning and 1D CNN, and the image feature was acquired through 2D CNN. According to the identification results, the accuracy of the text recognition model was 90%. The accuracy of the comparative model and the proposed model was 91.9% and 97.6%. The accuracy, precision, recall, and F1-score of the proposed model were better than those of the comparative model in terms of statistical significance. As a result, we confirmed that the expansion of the range of feature improved the performance of the identification model.

• Imaging Science in Dentistry
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• v.51 no.2
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• pp.187-193
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• 2021

Purpose: This study aimed to assess the usefulness of machine learning and automation techniques to match pairs of panoramic radiographs for personal identification. Materials and Methods: Two hundred panoramic radiographs from 100 patients (50 males and 50 females) were randomly selected from a private radiological service database. Initially, 14 linear and angular measurements of the radiographs were made by an expert. Eight ratio indices derived from the original measurements were applied to a statistical algorithm to match radiographs from the same patients, simulating a semi-automated personal identification process. Subsequently, measurements were automatically generated using a deep neural network for image recognition, simulating a fully automated personal identification process. Results: Approximately 85% of the radiographs were correctly matched by the automated personal identification process. In a limited number of cases, the image recognition algorithm identified 2 potential matches for the same individual. No statistically significant differences were found between measurements performed by the expert on panoramic radiographs from the same patients. Conclusion: Personal identification might be performed with the aid of image recognition algorithms and machine learning techniques. This approach will likely facilitate the complex task of personal identification by performing an initial screening of radiographs and matching ante-mortem and post-mortem images from the same individuals.

• Journal of the Korea Society of Computer and Information
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• v.27 no.9
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• pp.191-203
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• 2022

In this paper, we developed a system that intelligently identifies skin image data from big data collected from social media Instagram and extracts standardized skin sample data for skin condition diagnosis and management. The system proposed in this paper consists of big data collection and analysis stage, skin image analysis stage, training data preparation stage, artificial neural network training stage, and skin image identification stage. In the big data collection and analysis stage, big data is collected from Instagram and image information for skin condition diagnosis and management is stored as an analysis result. In the skin image analysis stage, the evaluation and analysis results of the skin image are obtained using a traditional image processing technique. In the training data preparation stage, the training data were prepared by extracting the skin sample data from the skin image analysis result. And in the artificial neural network training stage, an artificial neural network AnnSampleSkin that intelligently predicts the skin image type using this training data was built up, and the model was completed through training. In the skin image identification step, skin samples are extracted from images collected from social media, and the image type prediction results of the trained artificial neural network AnnSampleSkin are integrated to intelligently identify the final skin image type. The skin image identification method proposed in this paper shows explain high skin image identification accuracy of about 92% or more, and can provide standardized skin sample image big data. The extracted skin sample set is expected to be used as standardized skin image data that is very efficient and useful for diagnosing and managing skin conditions.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.4
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• pp.76-85
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• 2011

In this paper, we propose a texture feature-based language identification using Gabor feature and wavelet-domain BDIP (block difference of inverse probabilities) and BVLC (block variance of local correlation coefficients) features. In the proposed method, Gabor and wavelet transforms are first applied to a test image. The wavelet subbands are next denoised by Donoho's soft-thresholding. The magnitude operator is then applied to the Gabor image and the BDIP and BVLC operators to the wavelet subbands. Moments for Gabor magnitude image and each subband of BDIP and BVLC are computed and fused into a feature vector. In classification, the WPCA (whitened principal component analysis) classifier, which is usually adopted in the face identification, searches the training feature vector most similar to the test feature vector. Experimental results show that the proposed method yields excellent language identification with rather low feature dimension for a document image DB.

• Journal of the Korean Institute of Landscape Architecture
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• v.13 no.1
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• pp.109-121
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• 1985

A study on the image identification of urban streetscape is valuable for illuminating identity that is not yet fully approached in the field of environmental design. This analysis of urban streetscape for image identification allows us to make a more detailed exploration of an important approaching methods in dealing with the structural characteristics of identity. As a matter of fact, the earlier indirect studies on this image identification were made by environmental designers, such as architectural and urban designer in the field of environmental perception and came to its environmental cognition & environmental pattern research with assistances by such researchers as K. Lynch A. Rapoport & Christopher Alexander. Through its environmental perception research, we can see its structural characteristics that is aesthetic & visual structural contents of physical environmental elements. And we can see its cognitive characteristics through the environmental cognitive research, that is continuity, territoriality, identity of place, uniqueness or individuality, meaning & symbolism. Through its environmental pattern research, we can see its physical, socio - economic, cultural and symbolic pattern identification contents, that is physical form of the city, style of the street, pattern of streetscape, socio- economic & geographical locality, arid life cycle, life style, common style of the behavior, cultural pattern of the activity, socio - cultural expression of the symbol. In these process, we can set up a set of the environmental design criterias from those three integral studies for identity. And for an environmental research, Tongsung-Ro around the CBD (central business district) in Taegu City was selected for a case study, because this streetscape is suitable for that approaching methods in this study.

• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.51-59
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• 2024

In this work, we focused on reducing the amount of image data to be sent by extracting and progressively sending prominent image features to high-performance computing systems taking into consideration the right amount of image data required by object identification application. We demonstrate that with our technique called Progressive Object Detection over a Lossless Network using Fragmented DCT Coefficients (Proficient), object identification applications can detect objects with at least 70% combined confidence level by using less than half of the image data.

• IEIE Transactions on Smart Processing and Computing
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• v.6 no.4
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• pp.262-268
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• 2017

Intelligent video surveillance systems have been developed to monitor global areas and find specific target objects using a large-scale database. However, person re-identification presents some challenges, such as pose change and occlusions. To solve the problems, this paper presents an improved person re-identification method using sparse representation and saliency-based dictionary construction. The proposed method consists of three parts: i) feature description based on salient colors and textures for dictionary elements, ii) orthogonal atom selection using cosine similarity to deal with pose and viewpoint change, and iii) measurement of reconstruction error to rank the gallery corresponding a probe object. The proposed method provides good performance, since robust descriptors used as a dictionary atom are generated by weighting some salient features, and dictionary atoms are selected by reducing excessive redundancy causing low accuracy. Therefore, the proposed method can be applied in a large scale-database surveillance system to search for a specific object.

• Journal of Sport and Applied Science
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• v.4 no.2
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• pp.45-51
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• 2020

Purpose: The purpose of this study is to investigate how sports fans' loyalty is built via CSR activities of professional sports teams. Furthermore, the study sought to suggest the model presenting the process of developing loyalty of sport fans by teams' CSR performance. Research design, data, and methodology: For this purpose, a survey was conducted on 450 professional sports fans through the convenience sampling method. A total of 357 of the data were used for the final analysis. Based on the collected data, frequency analysis, reliability analysis, confirmatory factor analysis, and structural equation model analysis were conducted. Results: The results showed that CSR activities contribute to building a positive image of team. Regarding fan identification, team image has also a positive effect on enhancing identification. The finding has supported the notion that attitudinal loyalty is enhanced by fan identification and further attitudinal loyalty significantly influences behavioural loyalty of fans. Conclusions: The results of this study explored the function of CSR of the teams on attitudinal and behavioural outcomes, loyalty. Moreover, the study suggested the constructual model presenting its role on enhancing fans' attitudes and behaviour affecting participation and consumption. Academic and practical implications were discussed for sport marketers and practitioners.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.5
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• pp.1025-1034
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• 2009

In steel making production line, steel slabs are given a unique identification number. This identification number, Slab management number(SMN), gives information about the use of the slab. Identification of SMN has been done by humans for several years, but this is expensive and not accurate and it has been a heavy burden on the workers. Consequently, to improve efficiency, automatic recognition system is desirable. Generally, a recognition system consists of text localization, text extraction, character segmentation, and character recognition. For exact SMN identification, all the stage of the recognition system must be successful. In particular, the text localization is great important stage and difficult to process. However, because of many text-like patterns in a complex background and high fuzziness between the slab and background, directly extracting text region is difficult to process. If the slab region including SMN can be detected precisely, text localization algorithm will be able to be developed on the more simple method and the processing time of the overall recognition system will be reduced. This paper describes about the slab region localization using SIFT(Scale Invariant Feature Transform) features in the image. First, SIFT algorithm is applied the captured background and slab image, then features of two images are matched by Nearest Neighbor(NN) algorithm. However, correct matching rate can be low when two images are matched. Thus, to remove incorrect match between the features of two images, geometric locations of the matched two feature points are used. Finally, search rectangle method is performed in correct matching features, and then the top boundary and side boundaries of the slab region are determined. For this processes, we can reduce search region for extraction of SMN from the slab image. Most cases, to extract text region, search region is heuristically fixed [1][2]. However, the proposed algorithm is more analytic than other algorithms, because the search region is not fixed and the slab region is searched in the whole image. Experimental results show that the proposed algorithm has a good performance.