• IEIE Transactions on Smart Processing and Computing
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• 제4권4호
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• pp.281-290
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• 2015

Human action recognition from a video scene has remained a challenging problem in the area of computer vision and pattern recognition. The development of the low-cost RGB depth camera (RGB-D) allows new opportunities to solve the problem of human action recognition. In this paper, we present a comprehensive review of recent approaches to human action recognition based on depth maps, skeleton joints, and other hybrid approaches. In particular, we focus on the advantages and limitations of the existing approaches and on future directions.

• 로봇학회논문지
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• 제1권1호
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• pp.36-45
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• 2006

In this paper, we introduce visual contexts in terms of types and utilization methods for robust object recognition with intelligent mobile robots. One of the core technologies for intelligent robots is visual object recognition. Robust techniques are strongly required since there are many sources of visual variations such as geometric, photometric, and noise. For such requirements, we define spatial context, hierarchical context, and temporal context. According to object recognition domain, we can select such visual contexts. We also propose a unified framework which can utilize the whole contexts and validates it in real working environment. Finally, we also discuss the future research directions of object recognition technologies for intelligent robots.

• 한국융합학회논문지
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• 제5권4호
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• pp.1-7
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• 2014

동작 인식은 동작을 인식하여 처리하는 기술로 사용자에게 편이성과 직관성을 제공한다. 이러한 장점 때문에 동작 인식 기술은 군사, 의료, 교육 등 여러 분야에 융합되어 응용되고 있다. 특히, 게임 분야에서 동작 인식은 실제 동작과 유사하게 플레이할 수 있다는 장점 때문에, 의료, 군사, 교육 등의 분야와 융합되어지고 있다. 본 논문은 이러한 배경을 바탕으로 동작 인식 게임의 융합 발전 방향을 논하기 위한 것이다. 이를 위하여 본 논문에서는 동작 인식 기술 현황과 게임을 살펴보고 동작 인식 게임의 문제점과 개선 방안을 기술한다. 본 논문은 국내 동작 인식게임의 융합 경쟁력을 향상시키는데 도움을 줄 수 있다.

• 한국치위생학회지
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• 제10권2호
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• pp.335-344
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• 2010

Objectives : Targeting Korean adults, the food pattern are grasped. And, its correlation with oral disease is analyzed. In order to offer basic data to developing the nutritional policy and nutritional program for the future prevention from oral disease, a research was conducted by utilizing the Korean National Health and Nutrition Examination Survey 2005(the 3rd term). Methods : The subjects in this study were 6,526 adults in more than fully 19 years among 9,047 persons who participated in the food intake survey out of those who completed the health interview survey. The statistical analysis was analyzed by using SPSS 12.0 program. Results : 1. As a result of Group Analyzing was indicated to dangerous-type food pattern and protection-type food pattern. 2. As a result of analyzing the answers for having dental caries in the annually personal recognition was indicated to be high in the dangerous-type food pattern, and had not the statistically significant difference. 3. As a result of analyzing the answers for having periodontal disease in the annually personal recognition was indicated to be high in the dangerous-type food pattern, and had the statistically significant difference(p<0.05). 4. As a result of analyzing the food pattern factors that have influence upon both major oral illnesses in the annually personal recognition, the person, who has the dangerous-type food pattern, had high risk level of the periodontal disease in the annually personal recognition. Conclusions : In the above results, as a result of surveying and analyzing importance of the food pattern in the incidence of both major oral illnesses, it is considered that there will be necessity of continuing to research into developing the nutritional policy and nutritional program in order to prevent oral illness in the future.

• 한국ITS학회 논문지
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• 제18권6호
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• pp.241-250
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• 2019

도로표지는 도로 이용자를 위한 시설물로서 관리 및 유지보수의 편의성 증진을 위해 국토교통부에서는 관리시스템을 구축하여 운영 중에 있다. 향후 자율주행 시대에 도로표지의 역할은 감소하겠지만 그 필요성은 지속되고 있다. 이에 도로표지에 표기된 안내지명의 정확한 기계적 판독을 위해 도로표지 자동인식 장비를 개발하여 영상 기반의 문자 인식 기술을 적용하고 있지만 불규칙적인 규격과 수작업 제조, 조도, 빛반사, 강우 등 외부환경에 의해 오인식되는 경우가 다수 발생하고 있다. 본 연구에서는 영상 분석 등으로 극복할 수 없는 오인식 결과를 개선하기 위해 위치기반의 안내지명 후보를 도출하여 기준으로 하고, 오인식된 지명의 음소 분리를 통한 레벤슈타인 문자 유사도 검증 방법을 이용해 도로표지 안내지명 자동인식율을 개선하고자 하였다.

• 대한통합의학회지
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• 제3권1호
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• pp.63-69
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• 2015

Purpose: The purpose of this study was to investigate the differences in emotion recognition according to general characteristics of stroke patients. Method: The subjects consisted of 38 stroke patients receiving rehabilitation at S Hospital in Busan. Used the eMETT program to assess emotional cognition. Result: The age and duration of disease showed statistically significant differences in emotion recognition ability score, the gender and lesion showed a statistically significant difference in some emotion(p<.05). Conclusion: The results of this study it can be seen that the difference in emotion recognition ability in accordance with the general characteristics of the stroke. There will be a variety of future research related to standardized research or interventions targeted at stroke patients and normal controls to be carried out.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권2호
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• pp.929-944
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• 2019

Many critical applications require accurate real-time human action recognition. However, there are many hurdles associated with capturing and pre-processing image data, calculating features, and classification because they consume significant resources for both storage and computation. To circumvent these hurdles, this paper presents a recognition machine learning (ML) based system model which uses reduced data structure features by projecting real 3D skeleton modality on virtual 2D space. The MMU VAAC dataset is used to test the proposed ML model. The results show a high accuracy rate of 97.88% which is only slightly lower than the accuracy when using the original 3D modality-based features but with a 75% reduction ratio from using RGB modality. These results motivate implementing the proposed recognition model on an embedded system platform in the future.

• 자동차안전학회지
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• 제14권4호
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• pp.6-15
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• 2022

In this study, a traffic light recognition algorithm was implemented and validated for low-speed special purpose vehicles in an urban environment. Real-time image data using a camera and YOLO algorithm were applied. Two methods were presented to increase the accuracy of the traffic light recognition algorithm, and it was confirmed that the second method had the higher accuracy according to the traffic light type. In addition, it was confirmed that the optimal YOLO algorithm was YOLO v5m, which has over 98% mAP values and higher efficiency. In the future, it is thought that the traffic light recognition algorithm can be used as a dual system to secure the platform safety in the traffic information error of C-ITS.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권6호
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• pp.1540-1561
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• 2024

This research proposes a novel approach for Tamil Handwritten Character Recognition (THCR) that combines feature selection and ensemble learning techniques. The Tamil script is complex and highly variable, requiring a robust and accurate recognition system. Feature selection is used to reduce dimensionality while preserving discriminative features, improving classification performance and reducing computational complexity. Several feature selection methods are compared, and individual classifiers (support vector machines, neural networks, and decision trees) are evaluated through extensive experiments. Ensemble learning techniques such as bagging, and boosting are employed to leverage the strengths of multiple classifiers and enhance recognition accuracy. The proposed approach is evaluated on the HP Labs Dataset, achieving an impressive 95.56% accuracy using an ensemble learning framework based on support vector machines. The dataset consists of 82,928 samples with 247 distinct classes, contributed by 500 participants from Tamil Nadu. It includes 40,000 characters with 500 user variations. The results surpass or rival existing methods, demonstrating the effectiveness of the approach. The research also offers insights for developing advanced recognition systems for other complex scripts. Future investigations could explore the integration of deep learning techniques and the extension of the proposed approach to other Indic scripts and languages, advancing the field of handwritten character recognition.

• 한국융합학회논문지
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• 제11권6호
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• pp.1-6
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• 2020

본 연구에서는 컴퓨터 비전의 분야 중 하나인 문자 인식에 관한 연구를 수행했다. 대표적인 문자인식 기법 중 하나인 광학식 문자 판독 기법의 경우 일정한 규격과 서식에서 벗어나게 되면 인식률이 떨어진다는 한계점이 있다. 따라서 본 연구에서는 딥 러닝 기법을 적용해 이러한 문제점을 해결하고자 한다. 또한 기존의 문자 인식 연구의 경우 대부분 영어 및 숫자 인식에 국한되어 있다. 따라서 본 연구는 한글 인식을 위한 딥 러닝 기반 문자 인식 알고리즘을 제시한다. 알고리즘은 1-NED 평가 방법에서 0.841의 점수를 얻었으며, 이는 영어 인식 결과와 비슷한 수치이다. 본 연구를 통해 딥 러닝 기반 한글 인식 알고리즘의 성능을 확인할 수 있으며, 이를 통해 향후 연구방향에 대해 제시한다.