• International Journal of Computer Science & Network Security
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• 제23권12호
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• pp.115-122
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• 2023

Makkah Al-Mukarramah is the capital of Islamic world. It receives special attention from the Saudi government's rulers to transform it into a smart city for the benefit of millions of pilgrims. One of the 2030 vision objectives is to transform specific cities to smart ones with advanced technological facilitation, Makkah is one of these cities. The history of Makkah is not well known for some Muslims. As a result, we built the concepts of our application "Ain Makkah" to enable visitors of Makkah to know the history of Makkah by using technology. In particular "Ain Makkah" uses Augmented Reality to view the history of Al-Kaaba. A 3D model will overlay Al-Kaaba to show it in the last years. Our project will use Augmented Reality to build a 3D model to overlay Al-Kaaba. Future work will expand the number of historical landmarks of Makkah.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권2호
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• pp.676-692
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• 2018

Saliency detection in neurobiology is a vehement research during the last few years, several cognitive and interactive systems are designed to simulate saliency model (an attentional mechanism, which focuses on the worthiest part in the image). In this paper, a bottom up saliency detection model is proposed by taking into account the color and luminance frequency features of RGB, CIE $L^*a^*b^*$ color space of the image. We employ low-level features of image and apply band pass filter to estimate and highlight salient region. We compute the likelihood probability by applying Bayesian framework at pixels. Experiments on two publically available datasets (MSRA and SED2) show that our saliency model performs better as compared to the ten state of the art algorithms by achieving higher precision, better recall and F-Measure.

• 대한토목학회논문집
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• 제41권4호
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• pp.441-447
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• 2021

건설현장에서는 수많은 중장비와 작업자가 다양한 작업을 동시다발적으로 수행하기 때문에 복잡하고 위험한 상황이 자주 발생한다. 복잡한 현장에서 중장비가 단독으로 작업할 경우 운전자의 시야제한, 판단오류 등으로 인해 안전사고가 발생할 수 있으며, 이에 따라 중장비는 신호수와의 상호작용을 통해 주변 상황에 대한 정보를 수집하면서 작업을 수행해야 한다. 중장비를 자동으로 모니터링하고 위험상황을 탐지하기 위해 많은 컴퓨터비전 기술들이 개발되었지만, 기존의 방법들은 중장비 단독작업 인식에 필요한 중장비와 신호수 간 상호작용을 고려하지 않았다는 한계가 있다. 이러한 한계를 보완하기 위해 본 연구는 중장비-신호수 간의 상호작용을 고려한 컴퓨터비전 기반 중장비의 단독작업 자동 인식 모델을 제안함을 목표로 한다. 개발된 모델을 검증하기 위해 실제 건설현장으로부터 영상 데이터를 수집하여 실험을 수행하였다. 그 결과, 학습된 모델은 중장비와 사람을 83.4 %의 정확도로 인식하였고, 일반 작업자와 신호수를 84.2 %의 정확도로 분류하였으며, 중장비-신호수 간 상호작용 또한 95.1 %의 높은 정확도로 분석하였다. 본 연구결과는 건설현장에서 위험한 상황을 초래할 수 있는 중장비의 단독작업을 사전에 탐지하여 안전사고를 예방할 수 있다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1988년도 한국자동제어학술회의논문집(국제학술편); 한국전력공사연수원, 서울; 21-22 Oct. 1988
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• pp.810-815
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• 1988

This paper presents a mobile robot that travels employing visual information. The mobile robot is equipped solely with a TV camera as a sensor, and views from the TV camera are transferred to a separately installed micro computer through an image acquisition device. An acquired image of a view is processed there and the information necessary for travel is yielded. Instructions based on the information are then sent from the micro computer to the mobile robot, which causes the mobile robot next action. Among several application programs that have already been developed for the mobile robot other than the entire control program, this paper focuses its attention on the travelling control of the mobile robot in a model environment with obstructions as well as an overview of the whole system. The behaviour the present mobile robot takes when it travels among obstructions was investigated by an experiment, and satisfactory results were obtained.

• International Journal of Computer Science & Network Security
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• 제22권9호
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• pp.137-142
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• 2022

This paper argues for modern technologies in the educational process. It specifically outlines issues germane to virtual and augmented reality. It begins with an account on virtual reality and augmented reality, and touches on their characteristics, the advantages, obstacles and applications. It also discusses some relevant studies that emphasized the role of virtual and augmented reality in education, the difference between two terms. The paper ends with a note of vision on how to activate them in educational institutions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권9호
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• pp.4429-4447
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• 2018

Local binary descriptors are well-suited for many real-time and/or large-scale computer vision applications, while their low computational complexity is usually accompanied by the limitation of performance. In this paper, we propose a new optimization framework, RLDB (Robust-LDB), to improve a typical region-based binary descriptor LDB (local difference binary) and maintain its computational simplicity. RLDB extends the multi-feature strategy of LDB and applies a more complete region-comparing configuration. A cascade bit selection method is utilized to select the more representative patterns from massive comparison pairs and an online learning strategy further optimizes descriptor for each specific patch separately. They both incorporate LDP (linear discriminant projections) principle to jointly guarantee the robustness and distinctiveness of the features from various scales. Experimental results demonstrate that this integrated learning framework significantly enhances LDB. The improved descriptor achieves a performance comparable to floating-point descriptors on many benchmarks and retains a high computing speed similar to most binary descriptors, which better satisfies the demands of applications.

• 한국전자통신학회논문지
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• 제12권2호
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• pp.309-314
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• 2017

최근에는 가사 로봇, 자율주행 전기 자동차, 경영 효율성을 제고하기 위한 제철소 용선차의 자율 운행 분야가 큰 관심을 받고 있는데, 사람의 간섭 없이 전원을 로봇이나 차량에 공급하기 위한 자동 전원 공급 기술 개발이 문제가 되고 있다. 본 논문에서는 자동 전원 공급 기술의 기초 연구로서 주어진 공간에 있는 전원 컨넥터의 리셉터클을 인식하고 그것의 위치를 확인할 수 있는 영상인식 기반의 전원 컨넥터 리셉터클 위치 추적 기초 기술을 연구하며, 오픈 CV 프로그램을 통해서 그 기능성을 확인한다.

• International Journal of Advanced Culture Technology
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• 제5권1호
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• pp.51-57
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• 2017

Digital signage is becoming part of daily life across a wide range of visual advertisements segments market used in stations, hotels, retail stores, hotels, etc. The current digital signage system used in market is generally works on limited user interactivity with static contents. In this paper, a new approach is proposed using computer vision based dynamic audience adaptive cost-effective digital signage system. The proposed design uses the Camera attached Raspberry Pi Open source platform to employ the real-time audience interaction using computer vision algorithms to extract facial features of the audience. The real-time facial features are extracted using Haar Cascade algorithm which are used for audience gender specific rendering of dynamic digital signage content. The audience facial characterization using Haar Cascade is evaluated on the FERET database with 95% accuracy for gender classification. The proposed system, developed and evaluated with male and female audiences in real-life environments camera embedded raspberry pi with good level of accuracy.

• Journal of Information Processing Systems
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• 제17권6호
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• pp.1115-1126
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• 2021

Plant disease is one of the most irritating problems for agriculture growers. Thus, timely detection of plant diseases is of high importance to practical value, and corresponding measures can be taken at the early stage of plant diseases. Therefore, numerous researchers have made unremitting efforts in plant disease identification. However, this problem was not solved effectively until the development of artificial intelligence and big data technologies, especially the wide application of deep learning models in different fields. Since the symptoms of plant diseases mainly appear visually on leaves, computer vision and machine learning technologies are effective and rapid methods for identifying various kinds of plant diseases. As one of the fruits with the highest nutritional value, apple production directly affects the quality of life, and it is important to prevent disease intrusion in advance for yield and taste. In this study, an improved deep residual network is proposed for apple leaf disease identification in a novel way, a global residual connection is added to the original residual network, and the local residual connection architecture is optimized. Including that 1,977 apple leaf disease images with three categories that are collected in this study, experimental results show that the proposed method has achieved 98.74% top-1 accuracy on the test set, outperforming the existing state-of-the-art models in apple leaf disease identification tasks, and proving the effectiveness of the proposed method.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2021년도 추계학술대회
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• pp.317-319
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• 2021

국내에는 도로의 교통혼잡을 효율적으로 운영하기 위해서 지능형 교통체계(ITS)가 구축되어 있으며 교통정보 수집 및 과속단속 시스템에 활용되고 있다. 현재, 교통순환과 교통안전 확보를 위해 차로마다 통행 차량을 지정하는 지정차로제 및 전용차로제가 시행되고 있으며 인공지능 기술을 적용한 체계적이고 정확한 불법 차량 단속시스템이 필요하다. 본 연구에서는 지정차로제의 차량 통행의 효율성을 향상 할 수 있는 차량번호 인식 모델을 제안한다. 컴퓨터 비전 기술을 적용하여 실시간으로 3차선과 4차선의 다차선 도로를 인식하고 차로별 차량번호를 검지하여 지정차로제 위반 차량의 단속방안을 제시하고자 한다.