• 대한인간공학회지
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• 제32권2호
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• pp.207-215
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• 2013

Objective: This study aimed to investigate important human factors that should be considered when developing ultra-high definition TVs by reviewing measurement methods and main characteristics of ultra-high definition displays. Background: Although much attention has been paid to high-definition displays, there have been few studies for systematically evaluating human factors. Method: In order to determine human factors to be considered in developing human-friendly displays, we reviewed subjective and objective measurement methods to figure out the current limitations and establish a guideline for developing human-centered ultra-high definition TVs. In doing so, pros and cons of both subjective and objective measurement methods for assessing humans factors were discussed and specific aspects of ultra-high definition displays were also investigated in the literature. Results: Hazardous effects such as visually-induced motion sickness, visual fatigue, and mental fatigue in the brain caused by undesirable TV viewing are induced by not only temporal decay of visual function but also cognitive load in processing sophisticated external information. There has been a growing evidence that individual differences in visual and cognitive ability to process external information can make contrary responses after exposing to the same viewing situation. A wide vision, ultra-high definition TVs provide, can has positive and negative influences on viewers depending on their individual characteristics. Conclusion: Integrated measurement methods capable of considering individual differences in human visual system are required to clearly determine potential effects of super-high vision displays with a wide view on humans. All of brainwaves, autonomic responses, eye functions, and psychological responses should be simultaneously examined and correlated. Application: The results obtained in this review are expected to be a guideline for determining optimized viewing factors of ultra-high definition displays and accelerating successful penetration of the next-generation displays into our daily life.

• Journal of Advanced Marine Engineering and Technology
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• 제39권1호
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• pp.94-98
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• 2015

최근 정보화 및 컴퓨터 비전 기술의 발전과 함께 객체의 인식 및 추적 기능을 가진 CCTV시스템이 다양한 분야에서 연구되고 있다. 하지만 실외환경에서 발생할 수 있는 그림자의 변화, 조명의 변화, 움직이는 요소들과 같은 배경의 변화는 객체 인지성능에 영향을 주게 된다. 따라서 실외환경에서 배경의 변화를 실시간으로 갱신하기 위해 본 논문에서는 다양한 배경 모델링 기법들을 분석하고, 가중치를 기반으로 한 배경 갱신 알고리즘을 제안한다. 실험을 통해 제안한 알고리즘의 객체 검출 성능은 이전 연구의 객체 검출 성능을 유지하며, 오인식 된 객체 수가 이전 연구에 비해 감소됨을 확인하였다.

• 한국컴퓨터정보학회논문지
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• 제27권2호
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• pp.81-89
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• 2022

본 연구는 <데이비드 호크니>전의 인스타그램 게시물의 이미지를 분석하고, 이에 대한 시사점을 도출하는데 목적을 두었다. 본 연구는 인스타그램 게시물로부터 24,295개의 이미지를 크롤링했으며, 구글 클라우드 비전 API를 활용해서 라벨링을 진행했다. Word2Vec을 통해 총 212,567개의 라벨이 9개로 유형화되었으며, 사람 라벨 유형과 함께 미술관 공간, 포토존, 건축물 등의 빈도수가 높게 제시되었다. 결론적으로 관람객은 전시를 경험하면서 물리적 장소와 공간에 대한 경험과 기억을 큐레이팅했다. 이 결과는 사회적 현존감과 장소 만들기를 강조했던 선행 연구의 결과를 재 입증해 주었다. 본 연구에서 사용된 예술경영과 예술 공학의 융합적 접근방법론은 실무적 차원에서 박물관 및 미술관 전문 인력이 빅 데이터 기반 관람객 연구에 대한 통찰력을 획득하는데 도움을 줄 것으로 기대한다.

• Journal of Electrical Engineering and Technology
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• 제11권6호
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• pp.1857-1862
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• 2016

Human activity recognition using depth information is an emerging and challenging technology in computer vision due to its considerable attention by many practical applications such as smart home/office system, personal health care and 3D video games. This paper presents a novel framework of 3D human body detection, tracking and recognition from depth video sequences using spatiotemporal features and modified HMM. To detect human silhouette, raw depth data is examined to extract human silhouette by considering spatial continuity and constraints of human motion information. While, frame differentiation is used to track human movements. Features extraction mechanism consists of spatial depth shape features and temporal joints features are used to improve classification performance. Both of these features are fused together to recognize different activities using the modified hidden Markov model (M-HMM). The proposed approach is evaluated on two challenging depth video datasets. Moreover, our system has significant abilities to handle subject's body parts rotation and body parts missing which provide major contributions in human activity recognition.

• International Journal of Knowledge Content Development & Technology
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• 제10권2호
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• pp.51-69
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• 2020

This paper describes global research in the field of augmented reality (22078) as indexed in Scopus database during 1992-2019, using a series of bibliometric indicators. The augmented reality (AR) research registered high 54.23% growth, averaged citation impact of 8.90 citations per paper. Nearly 1% of global output in the subject (226 papers) registered high-end citations (100+) per paper. The top 15 countries accounted for 87.05% of global publications output in the subject. The USA is in leadership position for its highest publications productivity (19.25% global share). The U.K. leads the world on relative citation index (2.05). International collaboration has been a major driver of AR research pursuits; between 11.89% and 44.04% of national share of top 15 countries in AR research appeared as international collaborative publications. AR research productivity by application types was the largest across sectors, such as education, industry and medical. Computer science has emerged as the most popular areas in AR research pursuits. Technical University of Munich, Germany and Osaka University, Japan have been the most productive organizations and Nara Institute of S&T, Japan (66.55 and 7.48) and Imperial College, London, U.K. (57.14 and 6.42) have been the most impactful organizations. M. Billinghurst and N. Navab have been the most productive authors and S. Feiner and B. MacIntyre have been the most impactful authors. IEEE Transactions on Visualization & Computer Graphics, Multimedia Tools & Applications and Virtual Reality topped the list of most productive journals.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권11호
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• pp.5436-5458
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• 2017

Automatic Video Surveillance System (AVSS) has become important to computer vision researchers as crime has increased in the twenty-first century. As a new branch of AVSS, baggage detection has a wide area of security applications. Some of them are, detecting baggage in baggage restricted super shop, detecting unclaimed baggage in public space etc. However, in this paper, a detection & classification framework of baggage is proposed. Initially, background subtraction is performed instead of sliding window approach to speed up the system and HSI model is used to deal with different illumination conditions. Then, a model is introduced to overcome shadow effect. Then, occlusion of objects is detected using proposed mirroring algorithm to track individual objects. Extraction of rotational signal descriptor (SP-RSD-HOG) with support plane from Region of Interest (ROI) add rotation invariance nature in HOG. Finally, dynamic human body parameter setting approach enables the system to detect & classify single or multiple pieces of carried baggage even if some portions of human are absent. In baggage detection, a strong classifier is generated by boosting similarity measure based multi layer Support Vector Machine (SVM)s into HOG based SVM. This boosting technique has been used to deal with various texture patterns of baggage. Experimental results have discovered the system satisfactorily accurate and faster comparative to other alternatives.

• International Journal of Computer Science & Network Security
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• 제23권5호
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• pp.53-64
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• 2023

Early detection of endometrial carcinoma in uterus is essential for effective treatment. Endometrial carcinoma is the worst kind of endometrium cancer among the others since it is considerably more likely to affect the additional parts of the body if not detected and treated early. Non-invasive medical computer vision, also known as medical image processing, is becoming increasingly essential in the clinical diagnosis of various diseases. Such techniques provide a tool for automatic image processing, allowing for an accurate and timely assessment of the lesion. One of the most difficult aspects of developing an effective automatic categorization system is the absence of huge datasets. Using image processing and deep learning, this article presented an artificial endometrium cancer diagnosis system. The processes in this study include gathering a dermoscopy images from the database, preprocessing, segmentation using hybrid Fuzzy C-Means (FCM) and optimizing the weights using the Whale Optimization Algorithm (WOA). The characteristics of the damaged endometrium cells are retrieved using the feature extraction approach after the Magnetic Resonance pictures have been segmented. The collected characteristics are classified using a deep learning-based methodology called Long Short-Term Memory (LSTM) and Bi-directional LSTM classifiers. After using the publicly accessible data set, suggested classifiers obtain an accuracy of 97% and segmentation accuracy of 93%.

• ETRI Journal
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• 제35권6호
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• pp.1021-1028
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• 2013

Automatic facial expression recognition is a widely studied problem in computer vision and human-robot interaction. There has been a range of studies for representing facial descriptors for facial expression recognition. Some prominent descriptors were presented in the first facial expression recognition and analysis challenge (FERA2011). In that competition, the Local Gabor Binary Pattern Histogram Sequence descriptor showed the most powerful description capability. In this paper, we introduce hybrid facial representations for facial expression recognition, which have more powerful description capability with lower dimensionality. Our descriptors consist of a block-based descriptor and a pixel-based descriptor. The block-based descriptor represents the micro-orientation and micro-geometric structure information. The pixel-based descriptor represents texture information. We validate our descriptors on two public databases, and the results show that our descriptors perform well with a relatively low dimensionality.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 2009년도 IWAIT
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• pp.834-837
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• 2009

Augmented Reality (AR) is a useful technology for various industrial systems. This paper suggests a new playground system which uses markerless AR technology. We developed a virtual playground system that can learn physics and kinematics from the physical play of people. The virtual playground is a space in which real scenes and CG are mixed. As for the CG objects, physics of the real world is used. This is realized by a physics engine. Therefore it is necessary to analyze information from cameras, so that CG reflects the real world. Various games options are possible using real world images and physics simulation in the virtual playground. We think that the system is effective for education. Because CG behaves according to physics simulation, users can learn physics and kinematics from the system. We think that the system can take its place in the field of education through entertainment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제4권6호
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• pp.1194-1208
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• 2010

As visual surveillance systems gain wider usage in a variety of fields, it is important that they are capable of interpreting scenes automatically, also known as "human motion analysis" (HMA). However, existing HMA methods are too domain specific and computationally expensive. This paper proposes a general purpose HMA method that is based on the idea that human beings tend to exhibit erratic motion patterns during abnormal situations. Limb movements are characterized using the statistics of angular and linear displacements. In addition, the method is enhanced via the use of the entropy of the Fourier spectrum to measure the randomness of subject's motions. Various experiments have been conducted and the results indicate that the proposed method has very high classification accuracy in identifying anomalous behavior.