• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.5
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• pp.775-783
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• 1990

In this paper we implemented the fast thinning unit by modifying the pipeline architecture which was proposed by Stanley R. Sternberg. The unit is useful in preprocessing such as image representation and pattern recognition etc. This unit is composed of interface part, local memory part, address generation part, thinning processing part and control part. In thinning processing part, we shortened the thinning part which performed by means of look up table using window mapping table. Thus we improved the weakness of SAP, in which the number of delay pipeline and window pipeline are equal to image column size. Two independent memorys using tri-state buffer enable the two direction flow of address generated by address generation part. This unit avoids the complexity of architecture and has flexibility of image size by means of simple modification of logic bits.

• Journal of Information Processing Systems
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• v.10 no.3
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• pp.335-354
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• 2014

The accuracy of training-based activity recognition depends on the training procedure and the extent to which the training dataset comprehensively represents the activity and its varieties. Additionally, training incurs substantial cost and effort in the process of collecting training data. To address these limitations, we have developed a training-free activity recognition approach based on a fuzzy logic algorithm that utilizes a generic activity model and an associated activity semantic knowledge. The approach is validated through experimentation with real activity datasets. Results show that the fuzzy logic based algorithms exhibit comparable or better accuracy than other training-based approaches.

• Journal of Internet Computing and Services
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• v.22 no.1
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• pp.31-39
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• 2021

Obtaining delivery addresses from calls is one of the most important processes in TV home shopping business. By automating this process, the operational efficiency of TV home shopping can be increased. In this paper, a method of recognizing and validating road name address, which is the address system of South Korea, from speech oriented text is proposed. The speech oriented text has three challenges. The first is that the numbers are represented in the form of pronunciation. The second is that the recorded address has noises that are made from repeated pronunciation of the same address, or unordered address. The third is that the readability of the resulted address. For resolving these problems, the proposed method enhances the existing address databases provided by the Korea Post and Ministry of the Interior and Safety. Various types of pronouncing address are added, and heuristic rules for dividing ambiguous pronunciations are employed. And the processed address is validated by checking the existence in the official address database. Even though, this proposed method is for the STT result of the address pronunciation, this also can be used for any 3^rd party services that need to validate road name address. The proposed method works robustly on noises such as positions change or omission of elements.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.2
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• pp.120-126
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• 2020

Studies are being conducted regarding falling recognition using sensors on smartphonesto recognize falling in human daily life. These studies use a number of sensors, mostly acceleration sensors, gyro sensors, motion sensors, etc. Falling recognition system processes the values of sensor data by using a falling recognition algorithm and classifies behavior based on thresholds. If the threshold is ambiguous, the accuracy will be reduced. To solve this problem, Deep learning was introduced in the behavioral recognition system. Deep learning is a kind of machine learning technique that computers process and categorize input data rather than processing it by man-made algorithms. Thus, in this paper, we propose a falling recognition application system using deep learning based on smartphones. The proposed system is powered by apps on smartphones. It also consists of three layers and uses DataBase as a Service (DBaaS) to handle big data and address data heterogeneity. The proposed system uses deep learning to recognize the user's behavior, it can expect higher accuracy compared to the system in the general rule base.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.2
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• pp.785-804
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• 2017

Due to various factors such as postures, facial expressions and illuminations, face recognition by videos often suffer from poor recognition accuracy and generalization ability, since the within-class scatter might even be higher than the between-class one. Herein we address this problem by proposing a hierarchical cascaded classifier for video face recognition, which is a multi-layer algorithm and accounts for the misclassified samples plus their similar samples. Specifically, it can be decomposed into single classifier construction and multi-layer classifier design stages. In single classifier construction stage, classifier is created by clustering and the number of classes is computed by analyzing distance tree. In multi-layer classifier design stage, the next layer is created for the misclassified samples and similar ones, then cascaded to a hierarchical classifier. The experiments on the database collected by ourselves show that the recognition accuracy of the proposed classifier outperforms the compared recognition algorithms, such as neural network and sparse representation.

• Electronics and Telecommunications Trends
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• v.34 no.6
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• pp.133-144
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• 2019

Although deep learning-based visual image recognition technology has evolved rapidly, most of the commonly used methods focus solely on recognition accuracy. However, the demand for low latency and low power consuming image recognition with an acceptable accuracy is rising for practical applications in edge devices. For example, most Internet of Things (IoT) devices have a low computing power requiring more pragmatic use of these technologies; in addition, drones or smartphones have limited battery capacity again requiring practical applications that take this into consideration. Furthermore, some people do not prefer that central servers process their private images, as is required by high performance serverbased recognition technologies. To address these demands, the object and scene recognition technologies for mobile/embedded devices that enable optimized neural networks to operate in mobile and embedded environments are gaining attention. In this report, we briefly summarize the recent trends and issues of object and scene recognition technologies for mobile and embedded devices.

• Journal of the Korea Convergence Society
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• v.11 no.6
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• pp.1-6
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• 2020

In this study, research on character recognition, which is one of the fields of computer vision, was conducted. Optical character recognition, which is one of the most widely used character recognition techniques, suffers from decreasing recognition rate if the recognition target deviates from a certain standard and format. Hence, this study aimed to address this limitation by applying deep learning techniques to character recognition. In addition, as most character recognition studies have been limited to English or number recognition, the recognition range has been expanded through additional data training on Korean text. As a result, this study derived a deep learning-based character recognition algorithm for Korean text recognition. The algorithm obtained a score of 0.841 on the 1-NED evaluation method, which is a similar result to that of English recognition. Further, based on the analysis of the results, major issues with Korean text recognition and possible future study tasks are introduced.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.7
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• pp.2667-2682
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• 2015

Due to the wide application of face recognition (FR) in information security, surveillance, access control and others, it has received significantly increased attention from both the academic and industrial communities during the past several decades. However, partial face occlusion is one of the most challenging problems in face recognition issue. In this paper, a novel method based on linear regression-based classification (LRC) algorithm is proposed to address this problem. After all images are downsampled and divided into several blocks, we exploit the evaluator of each block to determine the clear blocks of the test face image by using linear regression technique. Then, the remained uncontaminated blocks are utilized to partial occluded face recognition issue. Furthermore, an improved Distance-based Evidence Fusion approach is proposed to decide in favor of the class with average value of corresponding minimum distance. Since this occlusion removing process uses a simple linear regression approach, the completely computational cost approximately equals to LRC and much lower than sparse representation-based classification (SRC) and extended-SRC (eSRC). Based on the experimental results on both AR face database and extended Yale B face database, it demonstrates the effectiveness of the proposed method on issue of partial occluded face recognition and the performance is satisfactory. Through the comparison with the conventional methods (eigenface+NN, fisherfaces+NN) and the state-of-the-art methods (LRC, SRC and eSRC), the proposed method shows better performance and robustness.

• Journal of the Korea Convergence Society
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• v.9 no.10
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• pp.339-346
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• 2018

The purpose of this study is to examine how visitors' recognition on cultural tourism festivals influences festival satisfaction and festival effects and suggest plans for operating the festivals. To address the goal, this author conducted a survey to visitors of memorable Chungjang Festival, and 441 sheets were finally used for positive analysis. According to the analysis results, the factors of recognition on cultural tourism festivals were divided into those related to recognition on tourism and recognition on culture, and these recognitions influence festival satisfaction and festival effects. Based on the results, this researcher suggests that it is important for those operating the festivals to make efforts to enhance tourism convenience for visitors and also develop programs which can allow visitors to learn about the unique culture of the region closely.

• Korean Journal of Health Education and Promotion
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• v.26 no.1
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• pp.141-158
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• 2009

Community-Based Participatory Research(CBPR) has gained attention as a public health approach to develop community health interventions to address health disparities in recognition of the community relevance of specific health issues associated with social determinants of health. It emphasizes community involvement in equal partnership with researchers and public health professionals to address community-identified needs. The characteristics and principles of CBPR discussed in this paper highlight participatory nature, capacity development, partnership building, and process-orientation of CBPR. A 6-step process model for community empowerment is then introduced as a CBPR operationalization strategy. Mixed methods research approaches are valuable in CBPR as well as process evaluation. For the application of CBPR in Korean contexts, the Diffusion of Innovation theory is suggested as a theoretical framework for implementation. Building public health partnerships between public and private sectors to create partnership synergy is a necessary condition for successful CBPR for health promotion in Korea. Accompanying critical factors for the CBPR application include: common understanding of CBPR and its values, establishment of the definition of 'community,' 'community-based' and 'participation' in community health, development of accommodating research infrastructure for CBPR, recognition of the importance of program evaluation (particularly process evaluation), and training CBPR specialists.