• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.9
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• pp.1744-1751
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• 2017

We are striving to strengthen environmental regulations and reduce household waste in all countries around the world. Korea is also striving for the circulation of energy resources by enacting laws to promote resource saving and recycling. The government has implemented an empty bottle deposit system for the recycling of empty bottles, but there is a limit to the collection through manpower and the reverse vending machine is not localized. In this paper, we propose a recyclable bottle recognition and classification algorithm which is essential in the reverser vending machine to promote energy resource circulation. The proposed algorithm is a complex identification algorithm using OpenCV and CNN(Convolution Neural Network). In order to evaluate the effectiveness of the proposed algorithm, we implement a classification system that operates in an reverse vending machine, so that it can easily acquire information about bottles and reverse vending machine in various devices.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.1
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• pp.453-458
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• 2016

One of the most common applications of machine vision is quality inspections in automated production. In this study, a welding inspection system that is controlled by a PC and a PLC equipped with a multi-camera setup was developed. The system was designed to measure the primary dimensions, such as the length and width of the welding areas. The TCP/IP protocols and multi-threading techniques were used for parallel control of the optical components and physical distribution. A coaxial light was used to maintain uniform lighting conditions and enhance the image quality of the weld areas. The core image processing system was established through a combination of various algorithms from the OpenCV library. The proposed vision inspection system was fully validated for an actual weld production line and was shown to satisfy the functional and performance requirements.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.9
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• pp.2153-2160
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• 2015

An analysis of hardware design conditions of moving object detection (MOD) algorithm is described, which is based on effective Gaussian mixture learning (EGML). A simulation model of EGML algorithm is implemented using OpenCV, and the effects of some parameter values on background learning time and MOD sensitivity are analyzed for various images. In addition, optimal design conditions for hardware implementation of EGML-based MOD algorithm are extracted from fixed-point simulations for various bit-widths of parameters. The proposed fixed-point model of the EGML-based MOD uses only half of the bit-width at the expense of the loss of MOD performance within 0.5% when compared with floating-point MOD results.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.10
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• pp.2443-2450
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• 2014

In the motion recognition system using depth image, the depth image is converted to the real world formed 3D point cloud data for efficient algorithm apply. And then, output depth image is converted by the projective world after algorithm apply. However, when coordinate conversion, rounding error and data loss by applied algorithm are occurred. In this paper, when convert 3D point cloud data to depth image, we proposed efficient conversion method and its hardware implementation without rounding error and data loss according image size change. The proposed system make progress using the OpenCV and the window program, and we test a system using the Kinect in real time. In addition, designed using Verilog-HDL and verified through the Zynq-7000 FPGA Board of Xilinx.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.05a
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• pp.661-664
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• 2009

Recently, intelligent surveillance camera systems are needed popularly. However, current researches are focussed on improvement of a single module rather than implementation of an integrated system. In this paper, we implemented a moving wireless surveillance camera system which is composed of face detection, and using motion sensor. In our implementation, we used a camera module from SHARP, a pair of wireless video transmission module from ECOM, body of moving robot used for A4WD1 Combo kit for RC, a pair of ZigBee RF wireless transmission module from ROBOBLOCK, and a motion sensor module (AMN14111) from PANASONIC. We used OpenCV library for face dection and MFC for implement software. We identified real-time operations of face detection, PTT control, and motion sensor detecton. Thus, the implemented system will be useful for the applications of remote control, human detection, and using motion sensor.

• Journal of Convergence for Information Technology
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• v.9 no.12
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• pp.17-23
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• 2019

In this paper, I propose a system development methodology that accepts images taken by camera attached to drone in real time while controlling mini drone and recognize and confirm the face of certain person. For the development of this system, OpenCV, Python related libraries and the drone SDK are used. To increase face recognition ratio of certain person from real-time drone images, it uses Deep Learning-based facial recognition algorithm and uses the principle of Triples in particular. To check the performance of the system, the results of 30 experiments for face recognition based on the author's face showed a recognition rate of about 95% or higher. It is believed that research results of this paper can be used to quickly find specific person through drone at tourist sites and festival venues.

• Journal of IKEEE
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• v.23 no.2
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• pp.358-363
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• 2019

In this paper, we implemented a welfare system for the elderly living alone, disabled, or babies to provide fast incident response in case of emergency situations. The proposed system can quickly recognize emergency situations using heart rate sensors and real-time image sharing. The sensors attached on a wrist band monitor the heart rate along with relevant bio signals of clients and send alarms to guardians in the emergency situations. At the same time, the real-time image signals are captured using OpenCV and sent to the guardians in order to give the exact information for fast and appropriate response to handle the situation. In the proposed system, the camera works only in the emergency situations so as to provide enough privacy to the client's every day life.

• The Journal of the Convergence on Culture Technology
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• v.5 no.1
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• pp.371-376
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• 2019

Creative thinking appears even before it is expressed in language, and its existence is revealed through emotion, intuition, image and body feeling before logic or linguistics rules work. In this study, Lego is intended to present experimental child interactive content that is applied with a computer vision based on image processing techniques. In the case of infants, the main purpose of this content is the development of hand muscles and the ability to implement imagination. The purpose of the analysis algorithm of the OpenCV library and the image processing using the 'VVVV' that is implemented as a 'Node' in the midst of perceptual changes in image processing technology that are representative of object recognition, and the objective is to use a webcam to film, recognize, derive results that match the analysis and produce interactive content that is completed by the user participating. Research shows what Lego children have made, and children can create things themselves and develop creativity. Furthermore, we expect to be able to infer a diverse and individualistic person's thinking based on more data.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.618-620
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• 2019

Smart Farm, which combines agriculture and ICT convergence technology, is at a lower stage than other industries in Korea, but it is also one of the most active research and development fields. Smart Farm aims to improve the efficiency of each step by collecting, processing and analyzing various information of agriculture sector through convergence between agriculture and ICT technology. In this study, we studied the image processing method that can distinguish strawberry which can be harvested at harvest time by color for smart farm composition of strawberry which is a horticultural crop. Strawberry harvesting requires a lot of labor in the process of growing strawberries. In this study, we aim to collect information necessary for labor saving in strawberry harvester. As a precedent study, we plan to implement a form in which the color temperature changes according to the light direction and brightness value through OpenCV color detection using Python. In the future, it is planned to study strawberry color value suitable for harvest by applying compensation value to color temperature change.

• Journal of the Korea Convergence Society
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• v.10 no.5
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• pp.17-26
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• 2019

We aimed to build a patient-based allergy prevention system using the smartphone and focused on the region of interest (ROI) extraction method for Optical Character Recognition (OCR) in the general environment. However, the current ROI extraction method has shown good performance in the experimental environment, but the performance in the real environment was not good due to the noisy background. Therefore, in this paper, we propose the compared methods of reducing noisy background to solve the ROI extraction problem. There five methods used as a SMF, DIN, Denoising Autoencoder(DAE), DAE with Convolution Neural Network(DAECNN) and median filter(MF) with DAECNN (MF+DAECNN). We have shown that our proposed DAECNN and MF+DAECNN methods are 69%, respectively, which is relatively higher than the conventional DAE method 55%. The verification of performance improvement uses MSE, PSNR and SSIM. The system has implemented OpenCV, C++ and Python, including its performance, is tested on real images.