• Journal of the Semiconductor & Display Technology
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• v.21 no.1
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• pp.75-78
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• 2022

Deep Neural Networks (DNN) has become an essential data processing architecture for the implementation of multiple computer vision tasks. Recently, DNN-based algorithms achieve much higher recognition accuracy than traditional algorithms based on shallow learning. However, training and inference DNNs require huge computational capabilities than daily usage purposes of computers. Moreover, with increased size and depth of DNNs, CPUs may be unsatisfactory since they use serial processing by default. GPUs are the solution that come up with greater speed compared to CPUs because of their Parallel Processing/Computation nature. In this paper, we analyze the inference time complexity of DNNs using well-known computer vision library, OpenCV. We measure and analyze inference time complexity for three cases, CPU, GPU-Float32, and GPU-Float16.

• International Journal of Computer Science & Network Security
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• v.24 no.6
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• pp.161-170
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• 2024

With the technological advances, the humans have made so much progress in the ease of living and now incorporating the use of sight, motion, sound, speech etc. for various application and software controls. In this paper, we have explored the project in which gestures plays a very significant role in the project. The topic of gesture control which has been researched a lot and is just getting evolved every day. We see the usage of computer vision in this project. The main objective that we achieved in this project is controlling the computer settings with hand gestures using computer vision. In this project we are creating a module which acts a volume controlling program in which we use hand gestures to control the computer system volume. We have included the use of OpenCV. This module is used in the implementation of hand gestures in computer controls. The module in execution uses the web camera of the computer to record the images or videos and then processes them to find the needed information and then based on the input, performs the action on the volume settings if that computer. The program has the functionality of increasing and decreasing the volume of the computer. The setup needed for the program execution is a web camera to record the input images and videos which will be given by the user. The program will perform gesture recognition with the help of OpenCV and python and its libraries and them it will recognize or identify the specified human gestures and use them to perform or carry out the changes in the device setting. The objective is to adjust the volume of a computer device without the need for physical interaction using a mouse or keyboard. OpenCV, a widely utilized tool for image processing and computer vision applications in this domain, enjoys extensive popularity. The OpenCV community consists of over 47,000 individuals, and as of a survey conducted in 2020, the estimated number of downloads exceeds 18 million.

• Journal of the Korea Convergence Society
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• v.7 no.4
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• pp.173-180
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• 2016

OpenCV is a programming language used in digital image processing and computer vision. In this study, look at media arts made using OpenCV programming language and find out about the utilization possibilities. To this end, the first, look at OpenCV functions that are frequently used in media art, the examples of utilizing the functions. The second, discuss media arts using OpenCV. focused on the OpenCV functions, programming language for an production of media art. The third, analyze features of media arts using OpenCV, mainly focused on the functions and programming languages. The study may provide guidance to the artists to produce a media art using the OpenCV or programming language.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.2
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• pp.211-218
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• 2021

Construction sites have various risk factors. There are various approaches to reduce safety accidents, but they have limitations to some extent. By utilizing the wireless communication technology of IT and the rapidly developing image processing technology, it will be possible to reduce accidents at the construction site if risk factors are identified and actively responded to. Therefore, in this study, a system that can detect risk factors of construction sites in advance is constructed, and a system is proposed to discover and respond to risk factors of construction sites using OpenCV for the purpose of real-time computer vision.

• Computers and Concrete
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• v.33 no.4
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• pp.425-443
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• 2024

Floods are among the most common natural hazards in urban areas. To mitigate the problems caused by flooding, unstructured data such as images and videos collected from closed circuit televisions (CCTVs) or unmanned aerial vehicles (UAVs) have been examined for flood management (FM). Many computer vision (CV) techniques have been widely adopted to analyze imagery data. Although some papers have reviewed recent CV approaches that utilize UAV images or remote sensing data, less effort has been devoted to studies that have focused on CCTV data. In addition, few studies have distinguished between the main research objectives of CV techniques (e.g., flood depth and flooded area) for a comprehensive understanding of the current status and trends of CV applications for each FM research topic. Thus, this paper provides a comprehensive review of the literature that proposes CV techniques for aspects of FM using ground camera (e.g., CCTV) data. Research topics are classified into four categories: flood depth, flood detection, flooded area, and surface water velocity. These application areas are subdivided into three types: urban, river and stream, and experimental. The adopted CV techniques are summarized for each research topic and application area. The primary goal of this review is to provide guidance for researchers who plan to design a CV model for specific purposes such as flood-depth estimation. Researchers should be able to draw on this review to construct an appropriate CV model for any FM purpose.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.763-764
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• 2008

Sensing is an important part of a smart home system. Vision sensors are a type of passive systems, which are not sensitive to noise. In this paper, we implement a prototype for stereo vision systems using OpenCV. It is an open source library for computer vision developed by Intel corporation. The prototype will by used for comparing performance among various stereo algorithms and for developing a stereo vision smart camera.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.9
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• pp.781-786
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• 2013

Multiplexing information over parallel data channels based on RF MIMO concept is possible to achieve considerable data rates over large transmission ranges with just a single transmitting element. Visual multiplexing MIMO techniques will send independent streams of bits using the multiple elements of the light transmitter array and recording over a group of camera pixels can further enhance the data rates. The proposed system is a combination of the reliance on computer vision algorithms for tracking and OOK cell frame modulation. LED array are controlled to transmit message in the form of digital information using ON-OFF signaling with ON-OFF pulses (ON = bit 1, OFF = bit 0). A camera captures image frames of the array which are then individually processed and sequentially decoded to retrieve data. To demodulated data transmission, a motion tracking algorithm is implemented in OpenCV (Open source Computer Vision library) to classify the transmission pattern. One of the most advantages of proposed architecture is Computer Vision (CV) based image analysis techniques which can be used to spatially separate signals and remove interferences from ambient light. It will be the future challenges and opportunities for mobile communication networking research.

• International Journal of Computer Science & Network Security
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• v.23 no.5
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• pp.193-206
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• 2023

In recent years, and with the increased adoption of digital transformation and spending long hours in front of these devices, clinicians have observed that the prolonged use of visual display units (VDUs) can result in a certain symptom complex, which has been defined as computer vision syndrome (CVS). This syndrome has been affected by many causes, such as light refractive errors, poor computer design, workplace ergonomics, and a highly demanding visual task. This research focuses on eliminating one of CVSs, which is the eye dry syndrome caused by infrequent eye blink rate while using a smart device for a long time. This research attempt to find a limitation on the current tools. In addition, exploring the other use cases to utilize the solution based on each vertical and needs.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.1
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• pp.117-124
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• 2021

Among growth disorders, 'Short Stature' can be improved through rapid diagnosis and treatment, and for that, it is important to detect early'Short Stature'. It is recommended to measure the height steadily for early detection of 'Short Stature' and checking the kid's growth process, but existing height measurement methods have problems such as time and space limitations, cost occurrence, and difficulty in keeping records. So in this paper, we proposed an 'Development of Kid Height Measurement Application based on Image using computer vision' method using smart phones, a medium that is highly accessible to people. In images taken through a smartphone camera, the kid's height is measured using algorithms from OpenCV, a computer vision library, and the measured heights were printed on the screen through 'a comparison graph with the standard height by gender and age' and 'list by date', made possible to check the kid's growth process. It is expected to measure height anytime, anywhere without time and space limitations and costs through this proposed method, and it is expected to help early detection of 'Short Stature' and other disorder through steady height measurement and confirmation of growth process.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.893-903
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• 2022

Recently, computer vision (CV)-based safety monitoring (i.e., object detection) system has been widely researched in the construction industry. Sufficient and high-quality data collection is required to detect objects accurately. Such data collection is significant for detecting small objects or images from different camera angles. Although several previous studies proposed novel data augmentation and synthetic data generation approaches, it is still not thoroughly addressed (i.e., limited accuracy) in the dynamic construction work environment. In this study, we proposed a game engine-driven synthetic data generation model to enhance the accuracy of the CV-based object detection model, mainly targeting small objects. In the virtual 3D environment, we generated synthetic data to complement training images by altering the virtual camera angles. The main contribution of this paper is to confirm whether synthetic data generated in the game engine can improve the accuracy of the CV-based object detection model.