• Journal of Biomedical Engineering Research
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• v.40 no.3
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• pp.105-115
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• 2019

Brain tumor surgery may be difficult, but it is also incredibly important. The technological improvements for traditional brain tumor surgeries have always been a focus to improve the precision of surgery and release the potential of the technology in this important area of the body. The need for precision during brain tumor surgery has led to an increase in Robotic-assisted surgeries (RAS). One of the challenges to the widespread acceptance of RAS in the neurosurgery is to recognize invisible tumor accurately. Therefore, it is important to detect brain tumor size and location because surgeon tries to remove as much tumor as possible. In this paper, we proposed brain tumor detection procedures for MRI (Magnetic Resonance Imaging) system. A method of automatic brain tumor detection is needed to accurately target the location of the lesion during brain tumor surgery and to report the location and size of the lesion. In the qualitative assessment, the proposed method showed better results than those obtained with other brain tumor detection methods. Comparisons among all assessment criteria indicated that the proposed method was significantly superior to the threshold method with respect to all assessment criteria. The proposed method was effective for detecting brain tumor.

• Journal of the Korean Society for Railway
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• v.10 no.6
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• pp.633-639
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• 2007

This study proposes an optimal condition to recognize a train using RFID. In order to recognize a moving train, bandwidth, an angle of antenna and the location of a tag should be considered. In this study, a field test was conducted using two different bandwidths (900MHz and 2.45GHz), four angles of antenna(0, 30, 45, and $60^{\circ}$), different velocities (10, 30 and 50km), and three different locations of tags. The field test verified the optimal condition for recognition of a train, The present study convinced that location detection and tracking of rail freight can be monitored in real time. The present technology can be applied to railway signals including detecting and tracking such as EURO Balis.

• Korean Journal of Construction Engineering and Management
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• v.12 no.6
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• pp.130-141
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• 2011

The intelligent excavating system(IES), the development in South Korea of which has been underway since 2006, aims for the full-scale automation of the excavation process that includes a series of tasks such as movement, excavation and loading. The core elements to ensure the quality and safety of the automated excavation equipment include 3D modeling of terrain that surrounds the excavating robot and the technology for detecting objects accurately(i.e., for detecting the location of nearby loading trucks and humans as well as of obstacles positioned on the movement paths). Therefore the purpose of this research is to ensure the quality and safety of automated excavation detecting the objects surrounding the excavating robot via a 3D laser scanning system. In this paper, an algorithm for estimating the location, height, width, and shape of objects in the 3D-realized terrain that surrounds the location of the excavator was proposed. The performance of the algorithm was verified via tests in an actual earthwork field.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.12
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• pp.71-83
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• 1996

This paper describes facial component detection and skew correction algorithm for face recognition. We use a priori knowledge and models about isolated regions to detect eye location from the face image captured in natural office environments. The relations between human face components are represented by several rules. We adopt an edge detection algorithm using sobel mask and 8-connected labelling algorith using array pointers. A labeled image has many isolated components. initially, the eye size rules are used. Eye size rules are not affected much by irregular input image conditions. Eye size rules size, and limited in the ratio between gorizontal and vertical sizes. By the eye size rule, 2 ~ 16 candidate eye components can be detected. Next, candidate eye parirs are verified by the information of location and shape, and one eye pair location is decided using face models about eye and eyebrow. Once we extract eye regions, we connect the center points of the two eyes and calculate the angle between them. Then we rotate the face to compensate for the angle so that the two eyes on a horizontal line. We tested 120 input images form 40 people, and achieved 91.7% success rate using eye size rules and face model. The main reasons of the 8.3% failure are due to components adjacent to eyes such as eyebrows. To detect facial components from the failed images, we are developing a mouth region processing module.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.1
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• pp.107-112
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• 2020

Recently, as the field of use of drones is diversified, it is actively used not only for surveying but also for search and rescue work. In these applications it is very important to know the location of the target or the location of the UAV. This paper proposes a target detection method using images taken from drones. The proposed method calculates the latitude and longitude information of the target by finding the location of the target by comparing it with the image to find the image taken by the drone. The exact latitude and longitude information of the target is calculated by calculating the actual distance corresponding to the distance of the image image using the characteristics of the pinhole camera. The proposed method through the actual experiment confirmed that the latitude and longitude of the target was accurately identified.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.1
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• pp.197-205
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• 2024

Development of AI and big data-based algorithms to advance and optimize the recognition and detection performance of various static/dynamic vehicles in front and around the vehicle at a time when securing driving safety is the most important point in the development and commercialization of autonomous vehicles. etc. are being studied. However, there are many research cases for recognizing the same vehicle by using the unique advantages of radar and camera, but deep learning image processing technology is not used, or only a short distance is detected as the same target due to radar performance problems. Therefore, there is a need for a convergence-based vehicle recognition method that configures a dataset that can be collected from radar equipment and camera equipment, calculates the error of the dataset, and recognizes it as the same target. In this paper, we aim to develop a technology that can link location information according to the installation location because data errors occur because it is judged as the same object depending on the installation location of the radar and CCTV (video).

• Journal of Korea Society of Industrial Information Systems
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• v.22 no.2
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• pp.1-13
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• 2017

In This Paper, we find the Target Candidate Region and the Location of the Candidate Region by Performing the Morphological Difference Calculation and Pixel Labeling for Robust Small Target Detection in Infrared Image with low SNR. Conventional Target Detection Methods based on Morphology Algorithms are low in Detection Accuracy due to their Vulnerability to Clutter in Infrared Images. To Address the Problem, Target Signal Enhancement and Background Clutter Suppression are Achieved Simultaneously by Combining Moravec Algorithm and LCM (Local Contrast Measure) Algorithm to Classify the Target and Noise in the Candidate Region. In Addition, the Proposed Algorithm can Efficiently Detect Multiple Targets by Solving the Problem of Limited Detection of a Single Target in the Target Detection method using the Morphology Operation and the Gaussian Distance Function Which were Developed for Real time Target Detection.

• Journal of the Korea Society of Computer and Information
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• v.26 no.7
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• pp.19-27
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• 2021

In this paper, we propose a method for detecting characters in images and detecting facial regions, which consists of two tasks. First, we separate two different characters to detect the face position of the characters in the frame. For fast detection, we use You Only Look Once (YOLO), which finds faces in the image in real time, to extract the location of the face and mark them as object detection boxes. Second, we present three image processing methods to detect accurate face area based on object detection boxes. Each method uses HSV values extracted from the region estimated by the detection figure to detect the face region of the characters, and changes the size and shape of the detection figure to compare the accuracy of each method. Each face detection method is compared and analyzed with comparative data and image processing data for reliability verification. As a result, we achieved the highest accuracy of 87% when using the split rectangular method among circular, rectangular, and split rectangular methods.

• Journal of Positioning, Navigation, and Timing
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• v.10 no.3
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• pp.169-177
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• 2021

In this paper, we propose a novel global navigation satellite system (GNSS) spoofing detection technique through an array antenna-based direction of arrival (DoA) estimation of satellite and spoofer. Specifically, we consider a sophisticated GNSS spoofing attack scenario where the spoofer can accurately mimic the multiple pseudo-random number (PRN) signals since the spoofer has its own GNSS receiver and knows the location of the target receiver in advance. The target GNSS receiver precisely estimates the DoA of all PRN signals using compressed sensing-based orthogonal matching pursuit (OMP) even with a small number of samples, and it performs spoofing detection from the DoA estimation results of all PRN signals. In addition, considering the initial situation of a sophisticated spoofing attack scenario, we designed the algorithm to have high spoofing detection performance regardless of the relative spoofing signal power. Therefore, we do not consider the assumption in which the power of the spoofing signal is about 3 dB greater than that of the authentic signal. Then, we introduce design parameters to get high true detection probability and low false alarm probability in tandem by considering the condition for the presence of signal sources and the proximity of the DoA between authentic signals. Through computer simulations, we compare the DoA estimation performance between the conventional signal direction estimation method and the OMP algorithm in few samples. Finally, we show in the sophisticated spoofing attack scenario that the proposed spoofing detection technique using OMP-based estimated DoA of all PRN signals outperforms the conventional spoofing detection scheme in terms of true detection and false alarm probability.

• Journal of Advanced Navigation Technology
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• v.16 no.2
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• pp.297-306
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• 2012

In this paper, we propose a Time Delay Traceback Scheme for the TDOA location estimation performance enhancement in NLOS environment and analyze the performance in various conditions. We place multiple readers in a square($300m{\times}300m$) searching area for reuse of received signal. Also, we use more active NLOS reader detection methode for NLOS error mitigation. when NLOS time delay 70 m, the number of the NLOS reader is 3 and the received sub-blinks number 3, proposed time delay trace-back scheme improve the RMSE about 16 m. From these results, we confirm that the proposed time delay traceback scheme is well-suited for the high precision location estimation to offer the location based service.