• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1202-1205
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• 2005

This paper describes a method for vision-based person identification that can detect, track, and recognize person from video using multiple cues: height and dressing colors. The method does not require constrained target's pose or fully frontal face image to identify the person. First, the system, which is connected to a pan-tilt-zoom camera, detects target using motion detection and human cardboard model. The system keeps tracking the moving target while it is trying to identify whether it is a human and identify who it is among the registered persons in the database. To segment the moving target from the background scene, we employ a version of background subtraction technique and some spatial filtering. Once the target is segmented, we then align the target with the generic human cardboard model to verify whether the detected target is a human. If the target is identified as a human, the card board model is also used to segment the body parts to obtain some salient features such as head, torso, and legs. The whole body silhouette is also analyzed to obtain the target's shape information such as height and slimness. We then use these multiple cues (at present, we uses shirt color, trousers color, and body height) to recognize the target using a supervised self-organization process. We preliminary tested the system on a set of 5 subjects with multiple clothes. The recognition rate is 100% if the person is wearing the clothes that were learned before. In case a person wears new dresses the system fail to identify. This means height is not enough to classify persons. We plan to extend the work by adding more cues such as skin color, and face recognition by utilizing the zoom capability of the camera to obtain high resolution view of face; then, evaluate the system with more subjects.

• Journal of Service Research and Studies
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• v.3 no.2
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• pp.53-60
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• 2013

Datamining from the large data in the form of various techniques for obtaining information have been developed. In recent years one of the most sought areas of pattern recognition and machine learning method is created with most of existing learning algorithms based on categorical attributes to a rule or decision model. However, the real-world data, it may consist of numeric attributes in many cases. In addition it contains attributes with numerical values to the normal categorical attribute. In this case, therefore, it is required processes in order to use the data to learn an appropriate value for the type attribute. In this paper, the domain of the numeric attributes are divided into several segments using learning algorithm techniques of discritization. It is described Clustering with other data mining techniques. Large amount of first cluster with characteristics is similar records from the database into smaller groups that split multiple given finite patterns in the pattern space. It is close to each other of a set of patterns that together make up a bunch. Among the set without specifying a particular category in a given data by extracting a pattern. It will be described similar grouping of data clustering technique to classify the data.

• Journal of the Korea Society for Simulation
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• v.19 no.1
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• pp.143-151
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• 2010

Fingerprint classification is a step to increase the efficiency of an 1:N fingerprint recognition system and plays a role to reduce the matching time of fingerprint and to increase accuracy of recognition. It is difficult to classify fingerprints, because the ridge pattern of each fingerprint class has an overlapping characteristic with more than one class, fingerprint images may include a lot of noise and an input condition is an exceptional case. In this paper, we propose a novel approach to design a stochastic model and to accomplish fingerprint classification using a directional characteristic of fingerprints for an effective classification of various qualities. We compute the directional value by searching a fingerprint ridge pixel by pixel and extract a directional characteristic by merging a computed directional value by fixed pixels unit. The modified Markov model of each fingerprint class is generated using Markov model which is a stochastic information extraction and a recognition method by extracted directional characteristic. The weight list of classification model of each class is decided by analyzing the state transition matrixes of the generated Markov model of each class and the optimized value which improves the performance of fingerprint classification using GA (Genetic Algorithm) is estimated. The performance of the optimized classification model by GA is superior to the model before the optimization by the experiment result of applying the fingerprint database of various qualities to the optimized model by GA. And the proposed method effectively achieved fingerprint classification to exceptional input conditions because this approach is independent of the existence and nonexistence of singular points by the result of analyzing the fingerprint database which is used to the experiments.

• Corrosion Science and Technology
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• v.18 no.6
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• pp.253-257
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• 2019

A deep learning based real-time painting surface inspection algorithm is proposed herein, designed for developing an autonomous inspection drone. The painting surface inspection is usually conducted manually. However, the manual inspection has a limitation in obtaining accurate data for correct judgement on the surface because of human error and deviation of individual inspection experiences. The best method to replace manual surface inspection is the vision-based inspection method with a camera, using various image processing algorithms. Nevertheless, the visual inspection is difficult to apply to surface inspection due to diverse appearances of material, hue, and lightning effects. To overcome technical limitations, a deep learning-based pattern recognition algorithm is proposed, which is specialized for painting surface inspections. The proposed algorithm functions in real time on the embedded board mounted on an autonomous inspection drone. The inspection results data are stored in the database and used for training the deep learning algorithm to improve performance. The various experiments for pre-inspection of painting processes are performed to verify real-time performance of the proposed deep learning algorithm.

• Journal of the Ergonomics Society of Korea
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• v.34 no.5
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• pp.411-426
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• 2015

Objective: The goal of this thesis is to design the interaction structure and framework of system to recognize sign language. Background: The sign language of meaningful individual gestures is combined to construct a sentence, so it is difficult to interpret and recognize the meaning of hand gesture for system, because of the sequence of continuous gestures. This being so, in order to interpret the meaning of individual gesture correctly, the interaction structure and framework are needed so that they can segment the indication of individual gesture. Method: We analyze 700 sign language words to structuralize the sign language gesture interaction. First of all, we analyze the transformational patterns of the hand gesture. Second, we analyze the movement of the transformational patterns of the hand gesture. Third, we analyze the type of other gestures except hands. Based on this, we design a framework for sign language interaction. Results: We elicited 8 patterns of hand gesture on the basis of the fact on whether the gesture has a change from starting point to ending point. And then, we analyzed the hand movement based on 3 elements: patterns of movement, direction, and whether hand movement is repeating or not. Moreover, we defined 11 movements of other gestures except hands and classified 8 types of interaction. The framework for sign language interaction, which was designed based on this mentioned above, applies to more than 700 individual gestures of the sign language, and can be classified as an individual gesture in spite of situation which has continuous gestures. Conclusion: This study has structuralized in 3 aspects defined to analyze the transformational patterns of the starting point and the ending point of hand shape, hand movement, and other gestures except hands for sign language interaction. Based on this, we designed the framework that can recognize the individual gestures and interpret the meaning more accurately, when meaningful individual gesture is input sequence of continuous gestures. Application: When we develop the system of sign language recognition, we can apply interaction framework to it. Structuralized gesture can be used for using database of sign language, inventing an automatic recognition system, and studying on the action gestures in other areas.

• Journal of Information Science Theory and Practice
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• v.11 no.4
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• pp.82-91
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• 2023

Receiving 100 citations from indexed journals is not a common occurrence, although it does happen very rarely, and it is even more uncommon for a non-indexed article. Given this, the purpose of this present study is to perform a bibliometric analysis of 100 cited Scopus articles to a non-Scopus indexed article, using a case study of the article titled "Authorship trends and collaboration pattern in the marine sciences literature: A scientometric study" which was published by Elango and Rajendran in the International Journal of Information Dissemination and Technology in 2012. On October 15, 2022, the Scopus database was searched with the article title in the references field, and the resulting bibliographic data was exported as a comma-separated values file. The tools utilized for this analysis were ScientoPy, the Bibliometrix R Package, and VOSviewer. Based on the findings, most of the cited articles were published within the last three years, and international researchers have given more recognition compared to Indian researchers. The most popular topics were found to be bibliometrics, bibliometric analysis, and bibliometrix. The fact that all 100 cited articles were published across various subject disciplines used by Scopus to categorise sources demonstrates how well-established the citing article is within the scientific community.

• Journal of KIBIM
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• v.13 no.3
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• pp.12-20
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• 2023

Deep learning-based anomaly detection technology is used in various fields such as computer vision, speech recognition, and natural language processing. In particular, this technology is applied in various fields such as monitoring manufacturing equipment abnormalities, detecting financial fraud, detecting network hacking, and detecting anomalies in medical images. However, in the field of construction and architecture, research on deep learning-based data anomaly detection technology is difficult due to the lack of digitization of domain knowledge due to late digital conversion, lack of learning data, and difficulties in collecting and processing field data in real time. This study acquires necessary data through IoT (Internet of Things) from the viewpoint of monitoring for environmental management of architectural spaces, converts them into a database, learns deep learning, and then supports anomaly patterns using AI (Artificial Infelligence) deep learning-based anomaly detection. We propose an implementation process. The results of this study suggest an effective environmental anomaly pattern detection solution architecture for environmental management of architectural spaces, proving its feasibility. The proposed method enables quick response through real-time data processing and analysis collected from IoT. In order to confirm the effectiveness of the proposed method, performance analysis is performed through prototype implementation to derive the results.

• Journal of the Earthquake Engineering Society of Korea
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• v.10 no.6 s.52
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• pp.1-8
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• 2006

This paper describes the procedures developing the algorithm for analyzing signals acquired from the Bridge Weigh-in-Motion (BWIM) system installed in Seohae Bridge as a part of the bridge monitoring system. Through the analysis procedure, information about heavy traffics such as weight, speed, and number of axles are attempted to be extracted from time domain strain data of the BWIM system. One of numerous pattern recognition techniques, artificial neural network (ANN) is employed since it can effectively include dynamic effects, bridge-vehicle interaction, etc. A number of vehicle running experiments with sufficient load cases are executed to acquire training and/or test set of ANN. Extracted traffic information can be utilized for developing quantitative database of loading effect. Also, it can contribute to estimate fatigue lift or current health condition, and design truck can be revised based on the database reflecting recent trend of traffic.

• Journal of the Korea Society of Computer and Information
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• v.14 no.9
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• pp.11-19
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• 2009

A RAM-based Neural Net(RBNN) which has multi-discriminators is more effective than RBNN with a discriminator. Experience Sensitive Cumulative Neural Network and 3-D Neuro System(3DNS) that accumulate the features point improved the performance of BNN, which were enabled to train additional and repeated patterns and extract a generalized pattern. In recognition process of Neural Net with multi-discriminator, the selection of class was decided by the value of MRD which calculates the accumulated sum of each class. But they had a saturation problem of its memory cells caused by learning volume increment. Therefore, the decision of MRD has a low performance because recognition rate is decreased by saturation. In this paper, we propose the method which improve the MRD ability. The method consists of the optimum MRD and the matching ratio prototype to generalized image, the cumulative filter ratio, the gap of prototype response MRD. We experimented the performance using NIST database of NIST without preprocessor, and compared this model with 3DNS. The proposed MRD method has more performance of recognition rate and more stable system for distortion of input pattern than 3DNS.

• ETRI Journal
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• v.32 no.3
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• pp.395-405
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• 2010

A novel multiclassifier system (MCS) strategy is proposed and applied to a text-dependent speaker verification task. The presented scheme optimizes the linear combination of classifiers on an on-line basis. In contrast to ordinary MCS approaches, neither a priori distributions nor pre-tuned parameters are required. The idea is to improve the most accurate classifier by making use of the incremental information provided by the second classifier. The on-line multiclassifier optimization approach is applicable to any pattern recognition problem. The proposed method needs neither a priori distributions nor pre-estimated weights, and does not make use of any consideration about training/testing matching conditions. Results with Yoho database show that the presented approach can lead to reductions in equal error rate as high as 28%, when compared with the most accurate classifier, and 11% against a standard method for the optimization of linear combination of classifiers.