• Current Optics and Photonics
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• v.1 no.6
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• pp.587-597
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• 2017

The extraction of the valid measurement region from the interference fringe pattern is a significant step when measuring gear tooth flank form deviation with grazing incidence interferometry, which will affect the measurement accuracy. In order to overcome the drawback of the conventionally used method in which the object image pattern must be captured, an improved segmentation approach is proposed in this paper. The interference fringe patterns feature, which is smoothed by the nonlinear diffusion, would be extracted by the structure tensor first. And then they are incorporated into the vector-valued Chan-Vese model to extract the valid measurement region. This method is verified in a variety of interference fringe patterns, and the segmentation results show its feasibility and accuracy.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.3
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• pp.248-253
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• 2008

This paper presents a sensing system for smart home which can detect an location transition events such as entrance/exit of a member and identify the user in a group at the same time. The proposed system is compose of two sub-systems; a wireless sensor network system and a database server system. The wireless sensing system is designed as a star network where each of sensing modules with ultrasonic sensors and a Bluetooth RF module connect to a central receiver called Bluetooth access point. We propose a method to discriminate a user by measuring the height of the user. The differences in the height of users is a key feature for discrimination. At the same time, the each sensing module can recognize whether the user goes into or out a room by using two ultrasonic sensors. The server subsystem is a sort of data logging system which read the detected event from the access point and then write it into a database system. The database system could provide the location transition information to wide range of context-aware applications for smart home easily and conveniently. We evaluate the developed method with experiments for three subjects in a family with the installation of the developed system into a real house.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.4
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• pp.368-374
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• 2011

This paper proposes a robot control human interface using Markov model (HMM) based hand signal recognizer. The command receiving humanoid robot sends webcam images to a client computer. The client computer then extracts the intended commanding hum n's hand motion descriptors. Upon the feature acquisition, the hand signal recognizer carries out the recognition procedure. The recognition result is then sent back to the robot for responsive actions. The system performance is evaluated by measuring the recognition of '48 hand signal set' which is created randomly using fundamental hand motion set. For isolated motion recognition, '48 hand signal set' shows 97.07% recognition rate while the 'baseline hand signal set' shows 92.4%. This result validates the proposed hand signal recognizer is indeed highly discernable. For the '48 hand signal set' connected motions, it shows 97.37% recognition rate. The relevant experiments demonstrate that the proposed system is promising for real world human-robot interface application.

• Journal of the Korean housing association
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• v.18 no.2
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• pp.113-120
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• 2007

This study is to investigate the contents and status of documentary records based on "Hyogyeongdang Gyechukmun" related to the Old House of Papyeong Yun's family. This house is located in Goegok-dong, Daejeon Metropolitan city, and is considered as one of the high-class houses in the mid-Chosun dynasty. The results might be summarized as follows: First, Hyogyeongdang Gyechukmun was written by Yun Seom in 1675 (the 1st year of King Sukjong's reign), which is a kind of general drawing book containing a plane figure and a bird's-eye-view of the old house. It is an important historical record to identify the feature and characteristics of the high-class houses in those days. Second, Papyeong Yun's Old House was founded with five buildings including a shrine, women's quarters, Hyogyeongdang, servants' quarters, and a warehouse. On the southern front, there used to be a pond. Third, the standard measure used to build the old house was about 310.00/尺(chuck). Chuck(尺) is the measuring unit of the Chosun Dynasty.

• Journal of KIISE:Information Networking
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• v.30 no.1
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• pp.30-37
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• 2003

In this paper, a discrete wavelet transform (DWT)-based watermarking system is proposed. The main feature of proposed system is that the embedding system uses adaptive thresholds to control the trade-off between the qualify of the watermarked image and the capacity of the watermark, and the trade-off between the quality and robustness of the watermarked image. Also, the extracting system rebuilds threshold according to various attacks and decides a watermark bit from the least distorted coefficient after measuring the distortion of coefficient. Finally, a new measure to detect the uniqueness of watermark is proposed. The experimental result shows that the proposed watermarking system is robust against conventional signal processing and intentional attacks.

• The KIPS Transactions:PartB
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• v.17B no.3
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• pp.207-214
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• 2010

A method of the region extraction and recognition of a traffic light and speed sign board in the real road environment is proposed. Traffic light was recognized by using brightness and color information based on HSI color model. Speed sign board was extracted by measuring red intensity from the HSI color information We improve the recognition rate by performing an incline compensation of the speed sign for directions clockwise and counterclockwise. The proposed algorithm shows a robust recognition rate in the image sequence which includes traffic light and speed sign board.

• Journal of Software Assessment and Valuation
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• v.16 no.2
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• pp.63-68
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• 2020

This paper proposes a face authentication system based on deep learning framework. The proposed system is consisted of face region detection and feature extraction using deep learning algorithm, and performed the face authentication using joint-bayesian matrix learning algorithm. The performance of proposed paper is evaluated by various face database , and the face image of one person consists of 2 images. The face authentication algorithm was performed by measuring similarity by applying 2048 dimension characteristic and combined Bayesian algorithm through Deep Neural network and calculating the same error rate that failed face certification. The result of proposed paper shows that the proposed system using deep learning and joint bayesian algorithms showed the equal error rate of 1.2%, and have a good performance compared to previous approach.

• International Journal of Aeronautical and Space Sciences
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• v.16 no.4
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• pp.581-589
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• 2015

This paper describes a monocular vision-based formation flight technology using two fixed wing unmanned aerial vehicles. To measuring relative position and attitude of a leader aircraft, a monocular camera installed in the front of the follower aircraft captures an image of the leader, and position and attitude are measured from the image using the KLT feature point tracker and POSIT algorithm. To verify the feasibility of this vision processing algorithm, a field test was performed using two light sports aircraft, and our experimental results show that the proposed monocular vision-based measurement algorithm is feasible. Performance verification for the proposed formation flight technology was carried out using the X-Plane flight simulator. The formation flight simulation system consists of two PCs playing the role of leader and follower. When the leader flies by the command of user, the follower aircraft tracks the leader by designed guidance and a PI control law, and all the information about leader was measured using monocular vision. This simulation shows that guidance using relative attitude information tracks the leader aircraft better than not using attitude information. This simulation shows absolute average errors for the relative position as follows: X-axis: 2.88 m, Y-axis: 2.09 m, and Z-axis: 0.44 m.

• International Journal of Computer Science & Network Security
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• v.24 no.9
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• pp.30-40
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• 2024

Building an accurate 3-D spatial road network model has become an active area of research now-a-days that profess to be a new paradigm in developing Smart roads and intelligent transportation system (ITS) which will help the public and private road impresario for better road mobility and eco-routing so that better road traffic, less carbon emission and road safety may be ensured. Dealing with such a large scale 3-D road network data poses challenges in getting accurate elevation information of a road network to better estimate the CO₂ emission and accurate routing for the vehicles in Internet of Vehicle (IoV) scenario. Clustering and regression techniques are found suitable in discovering the missing elevation information in 3-D spatial road network dataset for some points in the road network which is envisaged of helping the public a better eco-routing experience. Further, recently Explainable Artificial Intelligence (xAI) draws attention of the researchers to better interprete, transparent and comprehensible, thus enabling to design efficient choice based models choices depending upon users requirements. The 3-D road network dataset, comprising of spatial attributes (longitude, latitude, altitude) of North Jutland, Denmark, collected from publicly available UCI repositories is preprocessed through feature engineering and scaling to ensure optimal accuracy for clustering and regression tasks. K-Means clustering and regression using Support Vector Machine (SVM) with radial basis function (RBF) kernel are employed for 3-D road network analysis. Silhouette scores and number of clusters are chosen for measuring cluster quality whereas error metric such as MAE ( Mean Absolute Error) and RMSE (Root Mean Square Error) are considered for evaluating the regression method. To have better interpretability of the Clustering and regression models, SHAP (Shapley Additive Explanations), a powerful xAI technique is employed in this research. From extensive experiments , it is observed that SHAP analysis validated the importance of latitude and altitude in predicting longitude, particularly in the four-cluster setup, providing critical insights into model behavior and feature contributions SHAP analysis validated the importance of latitude and altitude in predicting longitude, particularly in the four-cluster setup, providing critical insights into model behavior and feature contributions with an accuracy of 97.22% and strong performance metrics across all classes having MAE of 0.0346, and MSE of 0.0018. On the other hand, the ten-cluster setup, while faster in SHAP analysis, presented challenges in interpretability due to increased clustering complexity. Hence, K-Means clustering with K=4 and SVM hybrid models demonstrated superior performance and interpretability, highlighting the importance of careful cluster selection to balance model complexity and predictive accuracy.

• Journal of Korea Multimedia Society
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• v.6 no.2
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• pp.247-258
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• 2003

Principal component analysis (PCA) based algorithms form the basis of numerous algorithms and studies in the face recognition literature. PCA is a statistical technique and its incorporation into a face recognition system requires numerous design decisions. We explicitly take the design decisions by in-troducing a generic modular PCA-algorithm since some of these decision ate not documented in the literature We experiment with different implementations of each module, and evaluate the different im-plementations using the September 1996 FERET evaluation protocol (the do facto standard method for evaluating face recognition algorithms). We experiment with (1) changing the illumination normalization procedure; (2) studying effects on algorithm performance of compressing images using JPEG and wavelet compression algorithms; (3) varying the number of eigenvectors in the representation; and (4) changing the similarity measure in classification process. We perform two experiments. In the first experiment, we report performance results on the standard September 1996 FERET large gallery image sets. The result shows that empirical analysis of preprocessing, feature extraction, and matching performance is extremely important in order to produce optimized performance. In the second experiment, we examine variations in algorithm performance based on 100 randomly generated image sets (galleries) of the same size. The result shows that a reasonable threshold for measuring significant difference in performance for the classifiers is 0.10.