• Journal of the Korean Institute of Electrical and Electronic Material Engineers
• /
• v.34 no.2
• /
• pp.121-125
• /
• 2021

The machine vision technology has been widely used in the industrialized nations like the United States, Japan, and EU in the various industries from the late 1980s. Machine vision inspection system mainly consists of a camera, optics, illumination and an image acquisition system. Optimization of the illumination light source is very important. This paper shows a comparison between Pulse Width Modulation (PWM) control and strobe control in driving LED lighting system for machine vision. PWM control method has problems such as a temperature rising of LED and a flickering in image measurement for inspection. In contrast, the proposed strobe control method can suppress the temperature of LED light source below 40℃. Also, it can remove the flickering problem through a synchronization between a frame grabber and a camera shutter. Finally, the strobe control method was shown to extract clearer images with a high precision compared to PWM control method.

• Nuclear Engineering and Technology
• /
• v.54 no.5
• /
• pp.1760-1768
• /
• 2022

When gamma-camera sensor modules, which are key components of radiation imagers, are derived from the coupling between scintillators and photosensors, the light collection efficiency is an important factor in determining the effectiveness with which the instrument can identify nuclides via their derived gamma-ray spectra. If the pixel area of the scintillator is larger than the pixel area of the photosensor, light loss and cross-talk between pixels of the photosensor can result in information loss, thereby degrading the precision of the energy estimate and the accuracy of the position-of-interaction determination derived from each active pixel in a coded-aperture based gamma camera. Here we present two methods to overcome the information loss associated with the loss of photons created by scintillation pixels that are coupled to an associated silicon photomultiplier pixel. Specifically, we detail the use of either: (1) light guides, or (2) scintillation pixel areas that match the area of the SiPM pixel. Compared with scintillator/SiPM couplings that have slightly mismatched intercept areas, the experimental results show that both methods substantially improve both the energy and spatial resolution by increasing light collection efficiency, but in terms of the image sensitivity and image quality, only slight improvements are accrued.

• Nuclear Engineering and Technology
• /
• v.53 no.6
• /
• pp.1947-1954
• /
• 2021

In the nuclear medicine imaging, quality control (QC) process using quadrant bar phantom is fundamental aspect of evaluating the spatial resolution. In addition, QC process of gamma camera is performed by daily or weekly. Recently, Monte Carlo simulation using the Geant4 application for tomographic emission (GATE) is widely applied in the pre-clinical nuclear medicine field for modeling gamma cameras with pixelated cadmium telluride (CdTe) semiconductor detector. In this study, we modeled a pixelated CdTe semiconductor detector and quadrant bar phantom (0.5, 1.0, 1.5, and 2.0 mm bar thicknesses) using the GATE tool. Similarity analysis based on correlation coefficients and peak signal-to-noise ratios was performed to compare image qualities for various source to collimator distances (0, 2, 4, 6, and 8 cm) and collimator lengths (0.2, 0.4, 0.6, 0.8, and 1.0 cm). To this end, we selected reference images based on collimator length and source to collimator distance settings. The results demonstrate that as the collimator length increases and the source to collimator distance decreases, the similarity to reference images improves. Therefore, our simulation results represent valuable information for the modeling of CdTe-based semiconductor gamma imaging systems and QC phantoms in the field of nuclear medicine.

• Annual Conference of KIPS
• /
• 2010.11a
• /
• pp.1784-1787
• /
• 2010

General Purpose Graphic Processing Unit (GPGPU) computing is a technique that utilizes the high-performance many-core processors of high-end graphic cards for general-purpose computations such as 3D graphics, video/image processing, computer vision, scientific computing, HPC and many more. GPGPUs offer a vast amount of raw computing power, but programming is extremely challenging because of hardware idiosyncrasies. The open computing language (OpenCL) has been proposed as a vendor-independent GPGPU programming interface. OpenCL is very close to the hardware and thus does little to increase GPGPU programmability. In this paper we present how a set of digital camera image signal processing (ISP) filters can be realized efficiently on GPGPUs using OpenCL. Although we found ISP filters to be memory-bound computations, our GPGPU implementations achieve speedups of up to a factor of 64.8 over their sequential counterparts. On GPGPUs, our proposed optimizations achieved speedups between 145% and 275% over their baseline GPGPU implementations. Our experiments have been conducted on a Geforce GTX 275; because of OpenCL we expect our optimizations to be applicable to other architectures as well.

• Journal of Korea Society of Industrial Information Systems
• /
• v.15 no.5
• /
• pp.159-166
• /
• 2010

From public insecurity, access of wealth, alteration of population structure, and changes of security recognition, physical security has been continuously developed and changed. In these days, typical systems for physical security are unmanned security system using telephone network and security equipment, image recognition system using DVR and camera, and access control system by finger print recognition and RFID cards. However, physical security system is broadening its domain towards ICT based convergence with networked camera, biometrics, individual authentication, and LBS services. This paper proposes main technical trends and various security convergences for future physical security services by classifying the security categories into 3 parts; Individual security for personal protection, IT Convergence for large buildings, and Homeland Security for omni-directional security.

• Proceedings of the KWS Conference
• /
• 2002.10a
• /
• pp.314-319
• /
• 2002

The Automation and Control Group at the University of Brasilia is developing an automatic welding station based on an industrial robot and a controllable welding machine. Several techniques were applied in order to improve the quality of the welding joints. This paper deals with the implementation of a laser-based computer vision system to guide the robotic manipulator during the welding process. Currently the robot is taught to follow a prescribed trajectory which is recorded a repeated over and over relying on the repeatability specification from the robot manufacturer. The objective of the computer vision system is monitoring the actual trajectory followed by the welding torch and to evaluate deviations from the desired trajectory. The position errors then being transfer to a control algorithm in order to actuate the robotic manipulator and cancel the trajectory errors. The computer vision systems consists of a CCD camera attached to the welding torch, a laser emitting diode circuit, a PC computer-based frame grabber card, and a computer vision algorithm. The laser circuit establishes a sharp luminous reference line which images are captured through the video camera. The raw image data is then digitized and stored in the frame grabber card for further processing using specifically written algorithms. These image-processing algorithms give the actual welding path, the relative position between the pieces and the required corrections. Two case studies are considered: the first is the joining of two flat metal pieces; and the second is concerned with joining a cylindrical-shape piece to a flat surface. An implementation of this computer vision system using parallel computer processing is being studied.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2022.01a
• /
• pp.211-213
• /
• 2022

Images in the textbooks influence the learning process. Students often see pictures before reading the text and these pictures can enhance the power of imagination of the students. The findings of some researches show that the images in textbooks can increase students' creativity. However, when learning major subjects, reading a textbook or looking at a picture alone may not be enough to understand the topics and completely realize the concepts. Studies show that viewers remember 95% of a message when watching a video than reading a text. If we can combine textbooks and videos, this teaching method is fantastic. The "TEXT + IMAGE + VIDEO (Animation)" concept could be more beneficial than ordinary ones. We tried to give our solution by using machine learning Image Classification. This paper covers the features, approaches and detailed objectives of our project. For now, we have developed the prototype of this project as a web app and it only works when accessed via smartphone. Once you have accessed the web app through your smartphone, the web app asks for access to use the camera. Suppose you bring your smartphone's camera closer to the picture in the textbook. It will then display the video related to the photo below.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2022.10a
• /
• pp.374-375
• /
• 2022

Recently, Metaverse service has been widely used to naturally communicate with a remote location, freeing from time and spatial constraints. In order to produce such contents, it is necessary to restore and synthesize a 3D model based on real space data. In this paper, a 3D-generated reconstruction model is produced based on continuous images using multiple cameras and a technique to correct the reconstructed 3D model is presented. For this. offline multi-camera setup was performed, errors were analyzed on the 3D model created through images obtained from various angles, and correction was performed using a matching technique between image frames. It is expected that 3D reconstructed data can be utilized in various service fields such as culture, tourism, and medical care.

• Smart Structures and Systems
• /
• v.32 no.5
• /
• pp.309-318
• /
• 2023

The appropriate maintenance of highway roads is critical for the safe operation of road networks and conserves maintenance costs. Multiple methods have been developed to investigate the surface of roads for various types of cracks and potholes, among other damage. Like road surface damage, the condition of expansion joints in concrete pavement is important to avoid unexpected hazardous situations. Thus, in this study, a new system is proposed for autonomous expansion joint monitoring using a vision-based system. The system consists of the following three key parts: (1) a camera-mounted vehicle, (2) indication marks on the expansion joints, and (3) a deep learning-based automatic evaluation algorithm. With paired marks indicating the expansion joints in a concrete pavement, they can be automatically detected. An inspection vehicle is equipped with an action camera that acquires images of the expansion joints in the road. You Only Look Once (YOLO) automatically detects the expansion joints with indication marks, which has a performance accuracy of 95%. The width of the detected expansion joint is calculated using an image processing algorithm. Based on the calculated width, the expansion joint is classified into the following two types: normal and dangerous. The obtained results demonstrate that the proposed system is very efficient in terms of speed and accuracy.

• Nuclear Engineering and Technology
• /
• v.55 no.10
• /
• pp.3700-3708
• /
• 2023

The stochastic origin ensembles method with resolution recovery (SOE-RR) has been proposed to reconstruct proton-induced prompt gammas (PGs), and the reconstructed PG image was used for range verification. However, due to low detection efficiency, the number of valid events is low. Such a low-count condition can degrade the accuracy of the SOE-RR method for proton range verification. In this study, we proposed two strategies to improve the reconstruction of the SOE-RR algorithm for low-count PG imaging. We also studied the number of iterations and repetitions required to achieve reliable range verification. We simulated a proton beam (10⁸ protons) irradiated on a water phantom and used a two-layer Compton camera to detect 4.44-MeV PGs. Our simulated results show that combining the SOE-RR algorithm with restricted volume (SOE-RR-RV) can reduce the error of the estimation of the Bragg peak position from 5.0 mm to 2.5 mm. We also found that the SOE-RR-RV algorithm initialized using a back-projection image could improve the convergence rate while maintaining accurate range verification. Finally, we observed that the improved SOE-RR algorithm set for 60,000 iterations and 25 repetitions could provide reliable PG images. Based on the proposed reconstruction strategies, the SOE-RR algorithm has the potential to achieve a positioning error of 2.5 mm for low-count PG imaging.