• International Journal of Fuzzy Logic and Intelligent Systems
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• 제13권2호
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• pp.124-132
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• 2013

This study investigated the feasibility of gait recognition using points on the body in three-dimensional (3D) space based on comparisons of four different feature vectors. To obtain the point trajectories on the body in 3D, gait motion data were captured from 10 participants using a 3D motion capture system, and four shoes with different heel heights were used to study the effects of heel height on gait recognition. Finally, the recognition rates were compared using four methods and different heel heights.

• International Journal of Control, Automation, and Systems
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• 제1권1호
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• pp.134-141
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• 2003

A major research issue associated with service robots is the creation of an environment recognition system for mobile robot navigation that is robust and efficient on various environment situations. In recent years, intelligent autonomous mobile robots have received much attention as the types of service robots for serving people and industrial robots for replacing human. To help people, robots must be able to sense and recognize three dimensional space where they live or work. In this paper, we propose a three dimensional environmental modeling method based on an edge enhancement technique using a planar fitting method and a neural network technique called "Growing Neural Gas Network." Input data pre-processing provides probabilistic density to the input data of the neural network, and the neural network generates a graphical structure that reflects the topology of the input space. Using these methods, robot's surroundings are autonomously clustered into isolated objects and modeled as polygon patches with the user-selected resolution. Through a series of simulations and experiments, the proposed method is tested to recognize the environments surrounding the robot. From the experimental results, the usefulness and robustness of the proposed method are investigated and discussed in detail.in detail.

• ETRI Journal
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• 제44권2호
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• pp.286-299
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• 2022

Human activity recognition in real time is a challenging task. Recently, a plethora of studies has been proposed using deep learning architectures. The implementation of these architectures requires the high computing power of the machine and a massive database. However, handcrafted features-based machine learning models need less computing power and very accurate where features are effectively extracted. In this study, we propose a handcrafted model based on three-dimensional sequential skeleton data. The human body skeleton movement over a frame is computed through joint positions in a frame. The joints of these skeletal frames are projected into two-dimensional space, forming a "movement polygon." These polygons are further transformed into a one-dimensional space by computing amplitudes at different angles from the centroid of polygons. The feature vector is formed by the sampling of these amplitudes at different angles. The performance of the algorithm is evaluated using a support vector machine on four public datasets: MSR Action3D, Berkeley MHAD, TST Fall Detection, and NTU-RGB+D, and the highest accuracies achieved on these datasets are 94.13%, 93.34%, 95.7%, and 86.8%, respectively. These accuracies are compared with similar state-of-the-art and show superior performance.

• 한국멀티미디어학회논문지
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• 제23권8호
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• pp.927-939
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• 2020

Gait recognition can identify people's identity from a long distance, which is very important for improving the intelligence of the monitoring system. Among many human features, gait features have the advantages of being remotely available, robust, and secure. Traditional gait feature extraction, affected by the development of behavior recognition, can only rely on manual feature extraction, which cannot meet the needs of fine gait recognition. The emergence of deep convolutional neural networks has made researchers get rid of complex feature design engineering, and can automatically learn available features through data, which has been widely used. In this paper,conduct feature metric learning in the three-dimensional space by combining the three-dimensional convolution features of the gait sequence and the Siamese structure. This method can capture the information of spatial dimension and time dimension from the continuous periodic gait sequence, and further improve the accuracy and practicability of gait recognition.

• 한국컴퓨터정보학회논문지
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• 제12권5호
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• pp.83-92
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• 2007

본 논문에서는 2차원 포인팅 장치의 한계를 극복하기 위하여 3차원 공간에서 주위환경에 관계없이 위치를 인지하고 좌표를 얻어낼 수 있는 관성항법시스템을 이용한 3차원 포인팅 기기의 설계 및 구현방식을 제안한다. 관성항법시스템은 각속도계(gyroscope)와 가속도계(accelerometer)의 데이터를 바탕으로 좌표를 계산하는 기법으로, 가속도계에서 발생하는 오차는 칼만 필터를 이용하여 데이터를 보정한다. 3차원 포인팅 장치의 프로토 타입 개발을 위해 무선 3차원 공간인식 마우스를 설계 및 구현하였으며, 디스플레이 장치에 표시를 위하여 RFIC를 이용하여 측정한 좌표 데이터를 수신 모듈로 전송하고 수신 모듈은 USB 드라이버를 통하여 호스트로 전달하였다. 본 논문은 관성항법시스템과 칼만 필터의 이론적인 지식을 바탕으로 3차원 포인팅 장치를 설계하고 프로토 타입을 구현하고 성능 평가를 통하여 3차원 공간에서 사용자의 움직임을 추출할 수 있는 입력 기기로서의 유용성을 검증하였으며 향후 유비쿼터스 컴퓨팅의 다양한 응용 장치로서의 가능성을 제시하였다.

• 한국정보처리학회논문지
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• 제6권3호
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• pp.821-831
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• 1999

3차운 물체 인식 중 오목과 볼록을 갖고 있는 물체의 인식은 대단히 어려운 문제이다. 본 논문에서는 물체의 인식을 위한 피처(Feature)의 추출 방법으로 오픈-볼(Open-Ball)을 제안한다. 이 새로운 방법은 물체의 크기, 이동고 회전에 불변성을 갖는 피처(Feature)를 생성하는 것뿐만이 아니라, 비교되는 물체를 인식하는 것을 상대적인 닮음 정도 측정으로 구현한다.

• 한국컴퓨터정보학회논문지
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• 제19권11호
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• pp.53-61
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• 2014

본 논문에서는 실내 환경의 3차원 공간에서 벽면을 분리해내기 위해 깊이 카메라로 받아들인 영상을 이용한 방법을 제안한다. 논문의 실험 결과에서 얻을 수 있는 정보를 이용하면 실내 공간을 인식하거나 그에 따른 인접한 물체의 탐색 또는 벽면에 프로젝터를 투사하는 등 3차원 공간 활용에 용이하다. 논문에서 제안하는 방법은 먼저 3차원 입력 영상에서의 좌표 점들을 이용하여 법선 벡터를 검출하고, 검출 된 법선 벡터를 비슷한 벡터들끼리의 그룹으로 나눈다. 나누어진 그룹들을 RANSAC을 이용하여 평면 단위로 분리한 후, 분리된 평면들은 실내 환경에서 알 수 있는 도메인 지식들에 기반 하여 벽면으로 구분 된다. 마지막으로 본 논문에서 제안하는 방법은 다양한 실험 환경을 통해 성능을 입증한다.

• Journal of Electrical Engineering and Technology
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• 제12권6호
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• pp.2388-2398
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• 2017

In this study, we propose a robust face recognition system to pose variations based on automatic pose estimation. Radial basis function neural network is applied as one of the functional components of the overall face recognition system. The proposed system consists of preprocessing and recognition modules to provide a solution to pose variation and high-dimensional pattern recognition problems. In the preprocessing part, principal component analysis (PCA) and 2-dimensional 2-directional PCA ($(2D)^2$ PCA) are applied. These functional modules are useful in reducing dimensionality of the feature space. The proposed RBFNNs architecture consists of three functional modules such as condition, conclusion and inference phase realized in terms of fuzzy "if-then" rules. In the condition phase of fuzzy rules, the input space is partitioned with the use of fuzzy clustering realized by the Fuzzy C-Means (FCM) algorithm. In conclusion phase of rules, the connections (weights) are realized through four types of polynomials such as constant, linear, quadratic and modified quadratic. The coefficients of the RBFNNs model are obtained by fuzzy inference method constituting the inference phase of fuzzy rules. The essential design parameters (such as the number of nodes, and fuzzification coefficient) of the networks are optimized with the aid of Particle Swarm Optimization (PSO). Experimental results completed on standard face database -Honda/UCSD, Cambridge Head pose, and IC&CI databases demonstrate the effectiveness and efficiency of face recognition system compared with other studies.

• Journal of Information Processing Systems
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• 제12권4호
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• pp.591-611
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• 2016

High dimensional space is the biggest problem when classification process is carried out, because it takes longer time for computation, so that the costs involved are also expensive. In this research, the facial space generated from homogeneous and non-homogeneous polynomial was proposed to extract the facial image features. The homogeneous and non-homogeneous polynomial-based eigenspaces are the second opinion of the feature extraction of an appearance method to solve non-linear features. The kernel trick has been used to complete the matrix computation on the homogeneous and non-homogeneous polynomial. The weight and projection of the new feature space of the proposed method have been evaluated by using the three face image databases, i.e., the YALE, the ORL, and the UoB. The experimental results have produced the highest recognition rate 94.44%, 97.5%, and 94% for the YALE, ORL, and UoB, respectively. The results explain that the proposed method has produced the higher recognition than the other methods, such as the Eigenface, Fisherface, Laplacianfaces, and O-Laplacianfaces.

• 한국컴퓨터그래픽스학회논문지
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• 제29권3호
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• pp.149-158
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• 2023

최신 기술이 적용된 HMD는 고해상도 디스플레이와 빠른 모션인식으로 멀미를 최소화하며 위치와 동작을 정확히 추적할 수 있다. 이는 가상의 삼차원 공간에 몰입할 수 있는 환경을 제공해 줄 수 있으며 이러한 특징을 활용하여 재난 시뮬레이터나 고위험 장비 학습 공간 등의 가상현실 콘텐츠가 발전하고 있다. 이러한 장점은 기초과학 교육 분야에서도 적용 가능하다. 특히 기존 2차원 자료로 설명되는 물리학의 전기장과 자기장의 개념을 삼차원 공간으로 확장하여 실시간으로 시각화한다면 학습 이해도 향상에 큰 도움이 될 수 있다. 본 논문에서는 삼차원 가상현실 기반의 실감형 물리 교육 환경 및 콘텐츠를 개발하고 개발된 학습 콘텐츠를 실제 학습 대상자에게 체험시켜 효과를 증명한다. 학습 대상자는 총 46명의 중학생과 대학생이며 가상현실 환경에서 삼차원으로 표현되는 전기장과 자기장을 실시간으로 경험하고 학습하였다. 설문 조사 결과 85% 이상의 긍정적인 반응을 얻을 수 있었으며 삼차원 가상공간 기반의 물리 학습이 효과적으로 적용될 수 있다는 긍정적인 결과를 얻었다.